43 datasets found
  1. YouTube Video Popularity Prediction Dataset

    • kaggle.com
    Updated Apr 1, 2025
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    Şahide ŞEKER (2025). YouTube Video Popularity Prediction Dataset [Dataset]. https://www.kaggle.com/datasets/sahideseker/youtube-video-popularity-prediction-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 1, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Şahide ŞEKER
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Area covered
    YouTube
    Description

    🇬🇧 English:

    This synthetic dataset is designed for predicting the popularity of YouTube videos using metadata. It includes fields like video title, duration, tags, and view count. Useful for regression modeling, feature engineering, and exploring social media analytics.

    Use this dataset to:

    • Build regression models to estimate video views.
    • Explore the impact of title length, tags, and duration on popularity.
    • Practice real-world machine learning tasks without using actual video data.

    Features:

    • video_id: Unique identifier for the video
    • title_length: Number of characters in the title
    • tags_count: Number of tags associated with the video
    • duration_sec: Duration of the video in seconds
    • views: Number of views (target variable)

    🇹🇷 Türkçe:

    Bu sentetik veri seti, YouTube videolarının popülerliğini (izlenme sayısını) tahmin etmek amacıyla oluşturulmuştur. Başlık uzunluğu, etiket sayısı ve video süresi gibi meta verileri içermektedir. Sosyal medya analizi ve regresyon modeli geliştirmek isteyenler için uygundur.

    Bu veri seti sayesinde:

    • Video izlenme sayısını tahmin eden regresyon modelleri geliştirilebilir.
    • Başlık uzunluğu, etiket sayısı ve sürenin popülerlik üzerindeki etkisi incelenebilir.
    • Gerçek video verisi kullanmadan makine öğrenmesi uygulamaları yapılabilir.

    Değişkenler:

    • video_id: Video için benzersiz kimlik
    • title_length: Başlık uzunluğu (karakter sayısı)
    • tags_count: Etiket sayısı
    • duration_sec: Süre (saniye cinsinden)
    • views: İzlenme sayısı (hedef değişken)
  2. YouTube Likes Prediction AV HackLive

    • kaggle.com
    Updated Oct 2, 2020
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    Vishal Gupta (2020). YouTube Likes Prediction AV HackLive [Dataset]. https://www.kaggle.com/jinxzed/youtube-likes-prediction-av-hacklive/metadata
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 2, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Vishal Gupta
    License

    http://www.gnu.org/licenses/old-licenses/gpl-2.0.en.htmlhttp://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html

    Area covered
    YouTube
    Description

    Context

    As YouTube becomes one of the most popular video-sharing platforms, YouTuber is developed as a new type of career in recent decades. YouTubers earn money through advertising revenue from YouTube videos, sponsorships from companies, merchandise sales, and donations from their fans. In order to maintain a stable income, the popularity of videos become the top priority for YouTubers. Meanwhile, some of our friends are YouTubers or channel owners in other video-sharing platforms. This raises our interest in predicting the performance of the video. If creators can have a preliminary prediction and understanding on their videos’ performance, they may adjust their video to gain the most attention from the public.

    You have been provided details on videos along with some features as well. Can you accurately predict the number of likes for each video using the set of input variables?

    Content

    Train Set

    video_id -> Identifier for each video

    title -> Name of the Video on Youtube

    channel_title -> Name of the Channel on Youtube

    category_id -> Category of the Video (anonymous)

    publish_date -> The date video was published

    tags -> Different tags for the video

    views -> Number of views received by the Video

    dislikes -> Number of dislikes on the Video

    comment_count -> Number on comments on the Video

    description -> Textual description of the Video

    country_code -> Country from which the Video was published

    likes -> Number of Likes on the video

    Acknowledgements

    Thank You Analytics Vidhya for providing this dataset.

  3. o

    Youtube Statistics

    • opendatabay.com
    .undefined
    Updated Jun 23, 2025
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    Datasimple (2025). Youtube Statistics [Dataset]. https://www.opendatabay.com/data/web-social/d9424c0c-a241-41e6-9a62-845ac9703e1b
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    .undefinedAvailable download formats
    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Datasimple
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Reviews & Ratings, YouTube
    Description

    This dataset contains two files for analyzing the relationship between the popularity of a certain video and the most relevant/liked comments of said video.

    File Descriptions videos-stats.csv: This file contains some basic information about each video, such as the title, likes, views, keyword, and comment count.

    comments.csv: For each video in videos-stats.csv, comments.csv contains the top ten most relevant comments as well as said comments' sentiments and likes.

    Column Descriptions videos-stats.csv:

    Title: Video Title. Video ID: The Video Identifier. Published At: The date the video was published in YYYY-MM-DD. Keyword: The keyword associated with the video. Likes: The number of likes the video received. If this value is -1, the likes are not publicly visible. Comments: The number of comments the video has. If this value is -1, the video creator has disabled comments. Views: The number of views the video got. comments.csv:

    Video ID: The Video Identifier. Comment: The comment text. Likes: The number of likes the comment received. Sentiment: The sentiment of the comment. A value of 0 represents a negative sentiment, while values of 1 or 2 represent neutral and positive sentiments respectively. Applicability Sentiment Analysis with comments Text Generation with comments Predicting video likes from comment information Popularity Analysis by Keyword Popularity Analysis Prediction video views from comment information/video statistics In-depth EDA of the Data

    Original Data Source: Youtube Statistics

  4. f

    Prediction mode distribution for depth maps and texture videos (%).

    • plos.figshare.com
    xls
    Updated Jun 21, 2023
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    Liquan Shen; Ping An; Zhi Liu (2023). Prediction mode distribution for depth maps and texture videos (%). [Dataset]. http://doi.org/10.1371/journal.pone.0171018.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Liquan Shen; Ping An; Zhi Liu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Prediction mode distribution for depth maps and texture videos (%).

  5. o

    Castro Video Metrics Dataset

    • opendatabay.com
    .undefined
    Updated Jul 5, 2025
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    Datasimple (2025). Castro Video Metrics Dataset [Dataset]. https://www.opendatabay.com/data/ai-ml/9a6bdd29-838d-4048-a278-62d22f47ba5a
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    .undefinedAvailable download formats
    Dataset updated
    Jul 5, 2025
    Dataset authored and provided by
    Datasimple
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Data Science and Analytics
    Description

    This dataset aims to preserve the digital legacy of Ernesto Castro, a notable millennial philosopher from Spain. It features content from his YouTube channel, including hundreds of hours of his classes, dialogues, and conferences, after his announcement in January 2025 regarding the potential cessation of his channel. The data provides valuable insights into his philosophical discourse and audience engagement metrics.

    Columns

    • ID_Video: The unique identification code for each YouTube video.
    • Titulo: The title given to the video.
    • Descripcion: A short explanation or summary of the video's content.
    • Canal: The name of the YouTube channel that uploaded the video.
    • FechaSubida: The specific calendar date when the video was uploaded.
    • URL: The direct web address (link) to the YouTube video.
    • Duracion: The length of the video, expressed in seconds.
    • Vistas: The total number of times the video has been viewed.
    • Likes: The count of positive reactions ('likes') the video has received.
    • Dislikes: The count of negative reactions ('dislikes') the video has received.
    • Comentarios: The numerical total of comments posted on the video.
    • QComentarios: (Likely representing Quantity of Comments) Another metric related to user comments on the video.
    • Transcripcion: The written transcript of the video's spoken content.
    • Usos: Information or notes pertaining to the usage or pre-processing of the video's data.

    Distribution

    The dataset is typically provided in a CSV (Comma Separated Values) file format. While specific numbers for rows or records are not explicitly available, the data covers various metrics such as video duration, views, likes, and dislikes. Video durations range from a minimum of 23 seconds up to a maximum of approximately 18,024 seconds (which is about 5 hours). View counts vary significantly, ranging from 595 up to over 521,000. Likes range from 0 to over 10,400, and dislikes range from 0 to over 4,200. The content originates primarily from one unique YouTube channel.

    Usage

    This dataset offers a foundation for several analytical applications: * Pre-processing: Useful for cleaning data, removing special characters, and tokenising text, preparing it for subsequent analysis. * Text Mining: Enables the analysis of keywords and the detection of prevalent themes within video descriptions and transcripts. * Natural Language Processing (NLP): Facilitates text modelling, sentiment analysis, automatic summary generation, and the classification of content. * Machine Learning (ML): Can be applied to predict audience engagement based on metrics such as views, likes, and dislikes, or to classify videos according to their descriptions and transcripts.

    Coverage

    The dataset has a global geographic scope. It includes content uploaded between 23rd April 2013 and 6th January 2025. The subject matter primarily revolves around philosophy, aesthetics, and academic discussions, with a particular emphasis on Spanish and Ibero-American thought.

    License

    CC0

    Who Can Use It

    This dataset is designed for a diverse audience, including: * Data Scientists and Analysts: For quantitative analysis of video performance and content trends. * Researchers and Academics: Those studying contemporary philosophy, digital culture, or media studies can explore discourse evolution. * Linguists and NLP Developers: For building and refining models related to language processing, content understanding, and summarisation. * Machine Learning Engineers: For training models to predict viewer interaction and categorise video content. * Students: A valuable resource for projects and studies in areas such as digital humanities, data science, and philosophy.

    Dataset Name Suggestions

    • Ernesto Castro YouTube Data Archive
    • Spanish Philosophy Video Analytics
    • Ernesto Castro Digital Thought
    • YouTube Philosophical Content
    • Castro Video Metrics Dataset

    Attributes

    Original Data Source: Ernesto Castro Dataset (ESP)

  6. w

    Global Video Bar Device Market Research Report: By Connectivity Type (USB,...

    • wiseguyreports.com
    Updated Aug 10, 2024
    + more versions
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    wWiseguy Research Consultants Pvt Ltd (2024). Global Video Bar Device Market Research Report: By Connectivity Type (USB, Bluetooth, Wi-Fi, Ethernet), By Resolution (1080p, 4K, 8K), By Field of View (90 degrees, 120 degrees, 180 degrees), By Features (Auto-Framing, Noise Reduction, Low-Light Enhancement), By Application (Video Conferencing, Live Streaming, Remote Collaboration) and By Regional (North America, Europe, South America, Asia Pacific, Middle East and Africa) - Forecast to 2032. [Dataset]. https://www.wiseguyreports.com/reports/video-bar-device-market
    Explore at:
    Dataset updated
    Aug 10, 2024
    Dataset authored and provided by
    wWiseguy Research Consultants Pvt Ltd
    License

    https://www.wiseguyreports.com/pages/privacy-policyhttps://www.wiseguyreports.com/pages/privacy-policy

    Time period covered
    Jan 8, 2024
    Area covered
    Global
    Description
    BASE YEAR2024
    HISTORICAL DATA2019 - 2024
    REPORT COVERAGERevenue Forecast, Competitive Landscape, Growth Factors, and Trends
    MARKET SIZE 20234.66(USD Billion)
    MARKET SIZE 20245.3(USD Billion)
    MARKET SIZE 203214.86(USD Billion)
    SEGMENTS COVEREDConnectivity Type ,Resolution ,Field of View ,Features ,Application ,Regional
    COUNTRIES COVEREDNorth America, Europe, APAC, South America, MEA
    KEY MARKET DYNAMICS1 Rising demand for remote collaboration 2 Increasing adoption in healthcare and education 3 Technological advancements in AI and machine learning 4 Growing popularity of hybrid work models 5 Expansion of video conferencing infrastructure
    MARKET FORECAST UNITSUSD Billion
    KEY COMPANIES PROFILEDLogitech ,Lenovo ,Barco ,Neat ,Yealink ,Jabra ,Zoom ,Aver ,Crestron ,HP ,Poly ,Microsoft ,Konftel ,Cisco ,Huddly
    MARKET FORECAST PERIOD2025 - 2032
    KEY MARKET OPPORTUNITIES1 Hybrid Workspaces 2 Remote Collaboration 3 Immersive Video Conferencing 4 Enhanced User Experience 5 Artificial Intelligence Integration
    COMPOUND ANNUAL GROWTH RATE (CAGR) 13.76% (2025 - 2032)
  7. d

    MoVi: A Large Multipurpose Motion and Video Dataset

    • search.dataone.org
    • borealisdata.ca
    Updated Nov 13, 2024
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    Ghorbani, Saeed; Mahdaviani, Kimia; Anne Thaler; Konrad Kording; Douglas James Cook; Gunnar Blohm; Nikolaus F. Troje (2024). MoVi: A Large Multipurpose Motion and Video Dataset [Dataset]. http://doi.org/10.5683/SP2/JRHDRN
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    Dataset updated
    Nov 13, 2024
    Dataset provided by
    Borealis
    Authors
    Ghorbani, Saeed; Mahdaviani, Kimia; Anne Thaler; Konrad Kording; Douglas James Cook; Gunnar Blohm; Nikolaus F. Troje
    Description

    MoVi is the first human motion dataset to contain synchronized pose, pose-dependent shape and video recordings. The MoVi database can be applied in human pose estimation and tracking, human motion prediction and synthesis, action recognition and gait analysis.

  8. S

    Smart View Systems Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Feb 13, 2025
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    Data Insights Market (2025). Smart View Systems Report [Dataset]. https://www.datainsightsmarket.com/reports/smart-view-systems-1534537
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    pdf, doc, pptAvailable download formats
    Dataset updated
    Feb 13, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    Smart View Systems Market Overview: The global smart view systems market is estimated to be valued at USD X.X million in 2025 and is projected to reach USD X.X million by 2033, exhibiting a CAGR of X.X% during the forecast period. The market is driven by increasing demand for enhanced security and surveillance solutions, growing adoption of smart home and commercial building automation systems, and rising awareness of the benefits of remote monitoring and video analytics. The market is segmented into applications (commercial, residential), types (short range systems, medium range systems, long range systems), and regions (North America, South America, Europe, Middle East & Africa, Asia Pacific). Market Drivers: The smart view systems market is primarily driven by the increasing demand for enhanced security and surveillance solutions. The growing adoption of smart home and commercial building automation systems is also contributing to the market growth. The rising awareness of the benefits of remote monitoring and video analytics is further fueling the demand for smart view systems. Additionally, government initiatives to promote smart city development and the integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) are expected to provide further impetus to the market growth.

  9. d

    Spelling Robotics Manipulation Dataset for \"See, Plan, Predict:...

    • search.dataone.org
    • borealisdata.ca
    Updated Dec 28, 2023
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    Attarian, Ioanna (2023). Spelling Robotics Manipulation Dataset for \"See, Plan, Predict: Language-guided Cognitive Planning with Video Prediction\" [Dataset]. http://doi.org/10.5683/SP3/VD9HXT
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    Dataset updated
    Dec 28, 2023
    Dataset provided by
    Borealis
    Authors
    Attarian, Ioanna
    Description

    The spelling simulation dataset for the paper "See, Plan, Predict: Language-guided Cognitive Planning with Video Prediction".

  10. V

    Video-sharing Social Networking Services Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated Feb 20, 2025
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    Archive Market Research (2025). Video-sharing Social Networking Services Report [Dataset]. https://www.archivemarketresearch.com/reports/video-sharing-social-networking-services-37817
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    pdf, ppt, docAvailable download formats
    Dataset updated
    Feb 20, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The video-sharing social networking services market is projected to grow from USD 39.3 billion in 2025 to USD 80.2 billion by 2033, exhibiting a CAGR of 9.2% during the forecast period. This growth can be attributed to the increasing popularity of video content, the proliferation of mobile devices, and the rising adoption of social media. The market is segmented based on type into less than 15 seconds, 15-30 seconds, 30-60 seconds, 1-5 minutes, 5-15 minutes, and greater than 15 minutes. The 15-30 seconds segment accounted for the largest share of the market in 2025, and it is expected to continue to dominate the market during the forecast period. This can be attributed to the growing popularity of short-form video content, such as TikTok and Instagram Reels. Based on application, the market is segmented into 13-20 Year Old, 20-30 Year Old, 30-40 Year Old, and over 40 Year Old. The 20-30 Year Old segment accounted for the largest share of the market in 2025, and it is expected to continue to dominate the market during the forecast period. Video-sharing social networking services (VSSNS) allow users to upload, share, and view videos. These services have become increasingly popular in recent years, with some of the largest platforms having billions of monthly active users.

  11. Movie Dataset - 800 movies

    • kaggle.com
    Updated Apr 13, 2025
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    Seniru Hasith (2025). Movie Dataset - 800 movies [Dataset]. https://www.kaggle.com/datasets/seniruhasith/movie-dataset-800-movies/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 13, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Seniru Hasith
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    🎬 Movie Success Prediction Dataset

    This dataset was curated to support machine learning models that predict movie success based on a wide range of multi-modal features, including cast popularity, sentiment analysis, audio-visual cues, social media engagement, and metadata such as budget and IMDb rating.

    📦 Dataset Overview

    The dataset consists of 36 engineered features extracted from various sources:

    • Cast and Crew Insights (e.g., popularity trends, number of cast members)
    • Sentiment Analysis from YouTube Comments using VADER
    • Audio Features from movie trailers using VGGish 3
    • Video Features using ResNet-based frame analysis
    • TikTok Popularity Signals (hashtags, views, engagement rate)
    • Movie Metadata (e.g., budget, IMDb rating)

    Each row represents one movie. The dataset is ideal for classification or regression tasks related to box office success, revenue prediction, or audience engagement forecasting.

    📊 Feature Mapping

    Feature CodeFeature Name
    Feature_1cast_trend_1
    Feature_2cast_trend_2
    Feature_3cast_trend_3
    Feature_4avg_cast_popularity
    Feature_5top_cast_popularity
    Feature_6genre_score
    Feature_7positive_sentiment
    Feature_8neutral_sentiment
    Feature_9negative_sentiment
    Feature_10num_youtube_comments
    Feature_11num_cast_members
    Feature_12num_upcoming_movies
    Feature_13avg_upcoming_popularity
    Feature_14max_upcoming_popularity
    Feature_15tiktok_hashtag_views
    Feature_16tiktok_video_count
    Feature_17tiktok_total_likes
    Feature_18tiktok_total_comments
    Feature_19tiktok_total_shares
    Feature_20tiktok_engagement_rate
    Feature_21audio_tempo
    Feature_22audio_energy_mean
    Feature_23audio_energy_variance
    Feature_24audio_spectral_centroid_mean
    Feature_25audio_spectral_rolloff_mean
    Feature_26video_brightness_mean
    Feature_27video_colorfulness_mean
    Feature_28video_scene_change_rate
    Feature_29video_emotion_happy
    Feature_30video_emotion_sad
    Feature_31imdb_rating
    Feature_32budget
    Feature_33log_budget
    Feature_34sqrt_budget
    Feature_35budget_squared
    Feature_36budget_rating_interaction

    🛠️ Feature Engineering Highlights

    • Audio features were extracted using the VGGish 3 model, widely used in speech emotion recognition tasks.
    • Video features were obtained from a ResNet-based model analyzing brightness, scene change rate, colorfulness, and emotion cues.
    • Sentiment scores were derived from YouTube comments using VADER, capturing positive, neutral, and negative sentiment proportions.
    • TikTok engagement metrics were collected using hashtag data, capturing likes, views, shares, and overall engagement rate.
    • Budget transformations such as log, square root, and squared values are included, along with an interaction feature with IMDb rating.

    💡 Potential Use-Cases

    • Predict box office revenue or success labels
    • Analyze which audio-visual cues correlate with public interest
    • Build early-stage predictors of movie success using trailers and social signals
    • Inform marketing strategies using real-time sentiment and TikTok trends

    📥 Data Sources

    • IMDb for metadata
    • YouTube (comments and trailers) for sentiment and audio/visual analysis
    • TikTok for hashtag popularity and engagement stats
    • In-house processing for video/audio feature extraction using ResNet and VGGish 3

    🚀 Whether you're working on predictive modeling, multimedia analysis, or social signal correlation, this dataset provides a diverse feature set to explore what makes a movie successful.

  12. Global Video Content Analytics (VCA) and Video Surveillance as a Service...

    • bisresearch.com
    csv, pdf
    Updated Jul 12, 2025
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    Bisresearch (2025). Global Video Content Analytics (VCA) and Video Surveillance as a Service (VSaaS) Market, Analysis and Forecast, 2017 – 2022 [Dataset]. https://bisresearch.com/industry-report/global-vsaas-market-report-forecast.html
    Explore at:
    csv, pdfAvailable download formats
    Dataset updated
    Jul 12, 2025
    Dataset authored and provided by
    Bisresearch
    License

    https://bisresearch.com/privacy-policy-cookie-restriction-modehttps://bisresearch.com/privacy-policy-cookie-restriction-mode

    Time period covered
    2023 - 2033
    Area covered
    Worldwide
    Description

    Global VCA and Video Surveillance as a Service (VSaaS) Market. This research report provides forecasts through 2022 and covers software, technologies, architecture, services and applications.

  13. Video Streaming Software Market Trends & Forecast 2026

    • thereportcubes.com
    Updated May 30, 2025
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    The Report Cube (2025). Video Streaming Software Market Trends & Forecast 2026 [Dataset]. https://www.thereportcubes.com/report-store/video-streaming-software-market
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    Dataset updated
    May 30, 2025
    Dataset provided by
    Authors
    The Report Cube
    License

    https://www.thereportcubes.com/privacy-policyhttps://www.thereportcubes.com/privacy-policy

    Area covered
    Global
    Description

    Explore the 2026 video streaming software market: growth drivers, key players, and future trends shaping the industry landscape.

  14. f

    Overall coding time saving compared with the state-of-the-art fast 3D-HEVC...

    • plos.figshare.com
    xls
    Updated May 30, 2023
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    Liquan Shen; Ping An; Zhi Liu (2023). Overall coding time saving compared with the state-of-the-art fast 3D-HEVC algorithms (%). [Dataset]. http://doi.org/10.1371/journal.pone.0171018.t007
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Liquan Shen; Ping An; Zhi Liu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Overall coding time saving compared with the state-of-the-art fast 3D-HEVC algorithms (%).

  15. Home Video Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
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    Dataintelo (2025). Home Video Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-home-video-market
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    csv, pdf, pptxAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Home Video Market Outlook



    The global home video market size reached an estimated value of $14.5 billion in 2023 and is projected to grow to approximately $21.7 billion by 2032, reflecting a Compound Annual Growth Rate (CAGR) of 4.5%. This market growth is largely driven by technological advancements and the increasing consumer demand for high-quality video experiences at home. With the proliferation of high-speed internet and the rise of streaming services, consumers are investing in more advanced home video equipment to enhance their viewing experience. Additionally, the trend towards home entertainment, catalyzed by global events such as the COVID-19 pandemic, has significantly impacted consumer behavior, encouraging more people to invest in home video technology.



    One of the primary growth factors of the home video market is the continuous advancement in video technology. The development of high-definition (HD), full high-definition (Full HD), and ultra-high-definition (4K) technologies has revolutionized the way consumers experience video content. Consumers are increasingly seeking out the latest technology to maximize their viewing experience, driving demand for advanced home video products. The competition among manufacturers to introduce innovative products with enhanced features plays a significant role in this growth. Furthermore, the introduction of new formats and codecs, which improve video compression and transmission quality, also contribute to the expansion of the market.



    The surge in popularity of streaming services is another critical factor propelling the home video market. Platforms like Netflix, Amazon Prime Video, Disney+, and Hulu have changed the way consumers access and view content, prioritizing convenience and on-demand accessibility. This shift has increased the demand for compatible home video devices that can seamlessly stream content in high quality. As more consumers cut the cable cord in favor of streaming, the demand for devices that support multiple streaming platforms is expected to continue growing. Additionally, the increasing production of exclusive content by streaming services has further incentivized consumers to upgrade their home video setups.



    Socio-economic factors also play a significant role in the growth of the home video market. As disposable incomes rise globally, particularly in emerging economies, consumers have more financial flexibility to purchase premium home video products. The growth of urbanization and a burgeoning middle class are contributing to increased consumption of entertainment products, including home video equipment. Additionally, changing consumer lifestyles, with a greater emphasis on home-based entertainment, have led to an increased focus on home video systems. Moreover, the trend towards smart homes and connected devices aligns with the growth of the home video market as more consumers seek integrated entertainment solutions.



    Consumer Video Services have become an integral part of the home video market's evolution. These services, which include streaming platforms and on-demand video options, offer consumers unprecedented access to a vast array of content. The convenience and flexibility of consumer video services have transformed viewing habits, allowing users to watch their favorite shows and movies anytime and anywhere. This shift has not only increased the demand for compatible home video devices but also encouraged content creators to produce high-quality, exclusive content to attract and retain subscribers. As a result, consumer video services continue to drive innovation and growth within the home video market, shaping the future of home entertainment.



    Regionally, the home video market growth is particularly robust in the Asia Pacific region, which is expected to exhibit the highest CAGR during the forecast period. This growth is fueled by rapid urbanization, increasing disposable incomes, and the burgeoning popularity of streaming services in countries like China and India. North America, with its technological advancements and high consumer spending power, remains a significant market, while Europe follows closely, driven by technological innovation and consumer demand for high-quality content. Latin America and the Middle East & Africa are also experiencing growth, although at a slower pace, due to the gradual adoption of advanced technologies and improvement in internet infrastructure.



    Product Type Analysis



    The home video market by product type includes

  16. D

    Distributed Video Processing System Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Jan 21, 2025
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    Data Insights Market (2025). Distributed Video Processing System Report [Dataset]. https://www.datainsightsmarket.com/reports/distributed-video-processing-system-1651531
    Explore at:
    ppt, doc, pdfAvailable download formats
    Dataset updated
    Jan 21, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global Distributed Video Processing System market size was valued at USD XXX million in 2025 and is projected to grow at a CAGR of XX% during the forecast period, reaching USD XXX million by 2033. The increasing adoption of video-based applications, including video conferencing, streaming, and surveillance, is a major driver of market growth. Additionally, the growing popularity of cloud-based video processing services is further fueling market expansion. Key trends in the Distributed Video Processing System market include the rising adoption of artificial intelligence (AI) and machine learning (ML) for video analysis, the increasing use of edge computing for real-time video processing, and the growing demand for low-latency video delivery. Major players in the market include InFocus, Beijing Tricolor Tech, Xunwei, Shenzhen KUANBO, Ningbo GQY, Shenzhen Xinshirui, CYSVC Tech, DigiBird, Shanghai Mics View, and Chartu Technologies Co. Asia Pacific is expected to be the fastest-growing regional market due to the rapid adoption of video-based applications and the presence of a large number of video content providers in the region.

  17. V

    Video-on-Demand Market Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Dec 3, 2024
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    Data Insights Market (2024). Video-on-Demand Market Report [Dataset]. https://www.datainsightsmarket.com/reports/video-on-demand-market-11212
    Explore at:
    ppt, pdf, docAvailable download formats
    Dataset updated
    Dec 3, 2024
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The size of the Video-on-Demand market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 10.66% during the forecast period.Video-on-Demand is a system which allows the choosing video contents, like movies and television programs, on demand. This does not give any of the rigid traditional broadcasting style of the televisions as the VODs allowed the viewer to do it his way. The view chose what they wanted to watch and when they wanted to view them and where they like to watch them.VOD services have changed the way people consume entertainment. Millions of viewers across the globe have managed to attract much attention through large libraries of content, including original series and movies, documentaries, and live sports. It can be reached through smart TVs, smartphones, tablets, and streaming devices.The main factors driving the growth of the VOD market include increased internet penetration, rising disposable incomes, and demand for streaming devices. As a matter of fact, the technological advancements that would make VOD services more complicated with high-definition video, personal recommendation features, and interactive content make the VOD market a continuously booming phenomenon in the future. Recent developments include: January 2023: FOX Entertainment and Hulu have announced a multi-year content partnership that includes in-season streaming rights for FOX's extensive programming schedule and a multi-platform strategic marketing alliance. All FOX primetime entertainment programming, from Family Guy and The Cleaning Lady to The Masked Singer and Next Level Chef, are expected to continue to stream on Hulu the day after its linear telecast, according to the terms of the agreement. Furthermore, the agreement includes a significant alliance in which FOX and Hulu branding will coexist across all FOX-owned and external marketing touchpoints to align FOX content's live and on-demand viewing messaging., June 2022: Amazon Prime Video, an over-the-top (OTT) platform, partnered with AMC Networks, a US-based entertainment company, to offer its content through Prime Video Channels in India. Furthermore, Amazon Prime Video Channels in India offer the ad-free subscription service AMC+ and AMC's streaming service Acorn TV on a subscription basis as part of the agreement.. Key drivers for this market are: Developments in Digital Video Landscape, Surge in Mobile Based Internet Users. Potential restraints include: Growing Threat of Video Content Piracy. Notable trends are: Surge in Mobile-based Internet Users to Drive the Market.

  18. Video Transcoding Market Analysis North America, APAC, Europe, Middle East...

    • technavio.com
    Updated Aug 5, 2024
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    Technavio (2024). Video Transcoding Market Analysis North America, APAC, Europe, Middle East and Africa, South America - US, China, Japan, UK, Mexico, India - Size and Forecast 2024-2028 [Dataset]. https://www.technavio.com/report/video-transcoding-market-industry-analysis
    Explore at:
    Dataset updated
    Aug 5, 2024
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2021 - 2025
    Area covered
    India, Japan, United Kingdom, Mexico, China, North America, United States, Global
    Description

    Snapshot img

    Video Transcoding Market Size 2024-2028

    The video transcoding market size is forecast to increase by USD 1.69 billion and is estimated to grow at a CAGR of 13.21% between 2023 and 2028. The digital landscape is undergoing significant transformation with the surge in content generation and the increasing popularity of Over-The-Top (OTT) platforms. The rise of live streaming has further fueled this trend, providing audiences with real-time access to their favorite content. The number of online content creators continues to grow, offering a diverse range of programming to cater to various tastes and preferences. This shift towards digital consumption is revolutionizing the media industry, enabling greater accessibility, convenience, and personalization for viewers. Content creators, in turn, benefit from the vast reach and engagement opportunities offered by OTT platforms, fostering a symbiotic relationship between technology and creativity.

    What will be the Size of the Market During the Forecast Period?

    For More Highlights About this Report, Request Free Sample

    Market Dynamic and Customer Landscape

    The Market is experiencing significant growth due to the increasing demand for OTT content on various devices. The market is driven by the proliferation of smart TVs, smartphones, and other multimedia mobile devices. Video transcoding plays a crucial role in ensuring compatibility and video quality for OTT content across different platforms. Video encoders are the backbone of video transcoding, converting video formats for streaming on various devices. Both hardware and software solutions are available in the market, with software-as-a-service solutions gaining popularity due to their flexibility and cost-effectiveness. Compatibility and video quality are the key factors influencing the market. Adaptive streaming and media asset management are essential features of video transcoding solutions, enabling seamless streaming on mobile devices and televisions. The telecommunication, IT and gaming, broadcasting, and content creator industries are major consumers of video transcoding solutions. The market is expected to continue its growth trajectory due to the increasing popularity of streaming services and the need for high-quality video content across various devices. Compression technology is a critical component of video transcoding, enabling efficient delivery of video content over the internet. The market for video transcoding is expected to remain dynamic, with ongoing advancements in technology and evolving consumer demands. Our researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

    Key Market Driver

    Increased content generation and rise of OTT platforms is notably driving market growth. The Video Transcoding Market is experiencing significant growth due to the increasing demand for OTT content on various video displaying devices such as Smart TVs, smartphones, tablets, and multimedia mobile devices. Communications Service Providers (CSPs) and content publishers are leveraging video encoders for compressing digital video files to ensure cross-platform compatibility and optimal video quality for streaming services.

    Moreover, the telecoms network landscape, particularly Long-Term Evolution (LTE), is facilitating the delivery of high-speed internet, enabling consumers to access high-quality video content on-demand. Software and hardware-based video transcoding solutions, as well as Software-as-a-Service (SaaS) offerings, are gaining popularity in the market. Thus, such factors are driving the growth of the market during the forecast period.

    Significant Market Trends

    Increasing adoption of AI-based video transcoding is the key trend in the market. The Market is experiencing significant growth due to the increasing adoption of smart TVs, smartphones, and Over-the-top (OTT) services. Video encoders are essential for converting OTT content into various formats suitable for multimedia mobile devices, tablets, and Telecoms network landscape. Both hardware and software solutions, including Software-as-a-service (SaaS), are utilized for video encoding.

    Moreover, compatibility and video quality are critical factors in the Video Transcoding Market. With the proliferation of streaming services and high-speed internet, there is a demand for adaptive streaming and compression to ensure seamless video playback on multiple devices, including mobile devices, televisions, and PCs. Cloud services have become a popular choice for media asset management and video encoding. Thus, such trends will shape the growth of the market during the forecast period.

    Major Market Challenge

    Increasing use of open-source and free editing software is the major challenge that affects the growth of the market. The Video Tra

  19. d

    Data from: Watching the watchers: Camera identification and characterization...

    • search.dataone.org
    • data.niaid.nih.gov
    • +1more
    Updated Mar 20, 2024
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    Trevor Seets; Alec Epstein; Andreas Velten (2024). Watching the watchers: Camera identification and characterization using retro-reflections - Dataset [Dataset]. http://doi.org/10.5061/dryad.6t1g1jx64
    Explore at:
    Dataset updated
    Mar 20, 2024
    Dataset provided by
    Dryad Digital Repository
    Authors
    Trevor Seets; Alec Epstein; Andreas Velten
    Description

    A focused imaging system such as a camera will reflect light directly back at a light source in a retro-reflection (RR) or cat-eye reflection. RRs provide a signal that is largely independent of distance providing a way to probe cameras at very long ranges. We find that RRs provide a rich source of information on a target camera that can be used for a variety of remote sensing tasks to characterize a target camera including predictions of rotation and camera focusing depth as well as cell phone model classification. We capture three RR datasets to explore these problems with both large commercial lenses and a variety of cell phones. This repository contains time-synced videos from the perspective of both a retro-reflective probe and a target camera that can be used to train algorithms for different remote sensing tasks. We include a dataset for cellphone classification, target camera rotation prediction, and target camera focusing depth prediction., , , # Watching the watchers: Camera identification and characterization using retro-reflections - Dataset

    https://doi.org/10.5061/dryad.6t1g1jx64

    Description of the data and file structure

    This dataset was used to generate results in the paper titled "Watching the watchers: Camera identification and characterization using retro-reflections"

    The data folder contains the datasets for the Phone classification and Focus_and_rotation experiments.

    data\Focus_Rotation\raw_data\angles: contains the paired videos from the viewpoint of the probe and target camera. The target camera video is used to calculate the rotation angle of the target camera. The probe video is the view from the probe and can be used to predict the target camera's rotation.

    data\Focus_Rotation\raw_data\focus contains videos for different settings of a commercial lens on a rotating target camera.

    data\PhoneData contains the data relevant for the phone classification experiment...

  20. TV & Video Audience Measurement Industry Trend | Video Audience Measurement...

    • emergenresearch.com
    pdf,excel,csv,ppt
    Updated May 6, 2022
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    Emergen Research (2022). TV & Video Audience Measurement Industry Trend | Video Audience Measurement Market Forecast by 2030 [Dataset]. https://www.emergenresearch.com/industry-report/video-audience-measurement-market
    Explore at:
    pdf,excel,csv,pptAvailable download formats
    Dataset updated
    May 6, 2022
    Dataset authored and provided by
    Emergen Research
    License

    https://www.emergenresearch.com/privacy-policyhttps://www.emergenresearch.com/privacy-policy

    Area covered
    Global
    Variables measured
    Base Year, No. of Pages, Growth Drivers, Forecast Period, Segments covered, Historical Data for, Pitfalls Challenges, 2030 Value Projection, Tables, Charts, and Figures, Forecast Period 2021 - 2030 CAGR, and 1 more
    Description

    Increasing number of viewers on Over-The-Top (OTT) media platforms and increasing video content among rapidly expanding audiences during and after COVID-19 lockdowns are key factors driving revenue growth of the global video audience measurement market | TV & Video Audience Measurement | Video Audie...

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Şahide ŞEKER (2025). YouTube Video Popularity Prediction Dataset [Dataset]. https://www.kaggle.com/datasets/sahideseker/youtube-video-popularity-prediction-dataset
Organization logo

YouTube Video Popularity Prediction Dataset

Predict video popularity using video metadata such as title, duration, and tags.

Explore at:
8 scholarly articles cite this dataset (View in Google Scholar)
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Apr 1, 2025
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Şahide ŞEKER
License

Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
License information was derived automatically

Area covered
YouTube
Description

🇬🇧 English:

This synthetic dataset is designed for predicting the popularity of YouTube videos using metadata. It includes fields like video title, duration, tags, and view count. Useful for regression modeling, feature engineering, and exploring social media analytics.

Use this dataset to:

  • Build regression models to estimate video views.
  • Explore the impact of title length, tags, and duration on popularity.
  • Practice real-world machine learning tasks without using actual video data.

Features:

  • video_id: Unique identifier for the video
  • title_length: Number of characters in the title
  • tags_count: Number of tags associated with the video
  • duration_sec: Duration of the video in seconds
  • views: Number of views (target variable)

🇹🇷 Türkçe:

Bu sentetik veri seti, YouTube videolarının popülerliğini (izlenme sayısını) tahmin etmek amacıyla oluşturulmuştur. Başlık uzunluğu, etiket sayısı ve video süresi gibi meta verileri içermektedir. Sosyal medya analizi ve regresyon modeli geliştirmek isteyenler için uygundur.

Bu veri seti sayesinde:

  • Video izlenme sayısını tahmin eden regresyon modelleri geliştirilebilir.
  • Başlık uzunluğu, etiket sayısı ve sürenin popülerlik üzerindeki etkisi incelenebilir.
  • Gerçek video verisi kullanmadan makine öğrenmesi uygulamaları yapılabilir.

Değişkenler:

  • video_id: Video için benzersiz kimlik
  • title_length: Başlık uzunluğu (karakter sayısı)
  • tags_count: Etiket sayısı
  • duration_sec: Süre (saniye cinsinden)
  • views: İzlenme sayısı (hedef değişken)
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