Video thumbnails are often the first things viewers see when they look for something interesting to watch. A strong, vibrant, and relevant thumbnail draws attention, giving viewers a quick preview of the content of the video, and helps them to find content more easily. Better thumbnails lead to more clicks and views for video creators.

Video thumbnails are often the first things viewers see when they look for something interesting to watch. A strong, vibrant, and relevant thumbnail draws attention, giving viewers a quick preview of the content of the video, and helps them to find content more easily. Better thumbnails lead to more clicks and views for video creators.

Inspired by the recent remarkable advances of deep neural networks (DNNs) in computer vision, such as image and video classification, our team has recently launched an improved automatic YouTube "thumbnailer" in order to help creators showcase their video content. Here is how it works.

The Thumbnailer Pipeline
While a video is being uploaded to YouTube, we first sample frames from the video at one frame per second. Each sampled frame is evaluated by a quality model and assigned a single quality score. The frames with the highest scores are selected, enhanced and rendered as thumbnails with different sizes and aspect ratios. Among all the components, the quality model is the most critical and turned out to be the most challenging to develop. In the latest version of the thumbnailer algorithm, we used a DNN for the quality model. So, what is the quality model measuring, and how is the score calculated?

The main processing pipeline of the thumbnailer.

(Training) The Quality Model
Unlike the task of identifying if a video contains your favorite animal, judging the visual quality of a video frame can be very subjective - people often have very different opinions and preferences when selecting frames as video thumbnails. One of the main challenges we faced was how to collect a large set of well-annotated training examples to feed into our neural network. Fortunately, on YouTube, in addition to having algorithmically generated thumbnails, many YouTube videos also come with carefully designed custom thumbnails uploaded by creators. Those thumbnails are typically well framed, in-focus, and center on a specific subject (e.g. the main character in the video). We consider these custom thumbnails from popular videos as positive (high-quality) examples, and randomly selected video frames as negative (low-quality) examples. Some examples of the training images are shown below.

Example training images.
The visual quality model essentially solves a problem we call "binary classification": given a frame, is it of high quality or not? We trained a DNN on this set using a similar architecture to the Inception network in GoogLeNet that achieved the top performance in the ImageNet 2014 competition.

Results
Compared to the previous automatically generated thumbnails, the DNN-powered model is able to select frames with much better quality. In a human evaluation, the thumbnails produced by our new models are preferred to those from the previous thumbnailer in more than 65% of side-by-side ratings. Here are some examples of how the new quality model performs on YouTube videos:

Example frames with low and high quality score from the DNN quality model, from video “Grand Canyon Rock Squirrel”.
Thumbnails generated by old vs. new thumbnailer algorithm.

We recently launched this new thumbnailer across YouTube, which means creators can start to choose from higher quality thumbnails generated by our new thumbnailer. Next time you see an awesome YouTube thumbnail, don’t hesitate to give it a thumbs up. ;)

Weilong Yang, software engineer, recently watched “Contact Juggling - His Skills are Totally Hypnotizing”
Min-hsuan Tsai, software engineer, recently watched ”People Are Awesome 2015”
Thanks to the Video Content Analysis and YouTube Creator teams

How to start developing

  • Choose your reports:
    • Video reports provide statistics for all user activity related to a channel's videos or a content owner's videos. For example, these metrics include the number of views or ratings that videos received. Some video reports for content owners also include earnings and ad performance metrics.
    • Playlist reports provide statistics that are specifically related to video views that occur in the context of a playlist.
    • Ad performance reports provide impression-based metrics for ads that ran during video playbacks. These metrics account for each ad impression, and each video playback can yield multiple impressions.
    • Estimated earnings reports provide the total earnings for videos from Google-sold advertising sources as well as from non-advertising sources. These reports also contain some ad performance metrics.
    • Asset reports provide user activity metrics related to videos that are linked to a content owners' assets. For its data to included in the report, a video must have been uploaded by the content owner and then claimed as a match of an asset in the YouTube Content ID system.
Want to get all of your YouTube data in bulk? Are you hitting the quota limits while accessing analytics data one request at a time? Do you want to be able to break down reports by more dimensions? What about accessing assets and revenue data?
With the new YouTube Bulk Reports API, your authorized application can retrieve bulk data reports in the form of CSV files that contain YouTube Analytics data for a channel or content owner. Once activated, reports are generated daily and contain data for a unique, 24-hour period.

While the known YouTube Analytics API supports real-time targeted queries of much of the same data as the YouTube Bulk Reports API, the latter is designed for applications that can retrieve and import large data sets, then use their own tools to filter, sort, and mine that data.

As of now the API supports video, playlist, ad performance, estimated earnings and asset reports.

How to start developing


  • Choose your reports:
    • Video reports provide statistics for all user activity related to a channel's videos or a content owner's videos. For example, these metrics include the number of views or ratings that videos received. Some video reports for content owners also include earnings and ad performance metrics.
    • Playlist reports provide statistics that are specifically related to video views that occur in the context of a playlist.
    • Ad performance reports provide impression-based metrics for ads that ran during video playbacks. These metrics account for each ad impression, and each video playback can yield multiple impressions.
    • Estimated earnings reports provide the total earnings for videos from Google-sold advertising sources as well as from non-advertising sources. These reports also contain some ad performance metrics.
    • Asset reports provide user activity metrics related to videos that are linked to a content owners' assets. For its data to included in the report, a video must have been uploaded by the content owner and then claimed as a match of an asset in the YouTube Content ID system.

  • Schedule reports:
  1. Get an OAuth token (authentication credentials)
  2. Call the reportTypes.list method to retrieve a list of the available report types
  3. Create a new reporting job by calling jobs.create and passing the desired report type (and/or query in the future)

  • Retrieve reports:
  1. Get an OAuth token (authentication credentials)
  2. Call the jobs.list method to retrieve a list of the available reporting jobs and remember its ID.
  3. Call the reports.list method with the jobId filter parameter set to the ID found in the previous step to retrieve a list of downloadable reports that that particular job created.
  4. Creators can check the report’s last modified date to determine whether the report has been updated since the last time it was retrieved.
  5. Fetch the report from the URL obtained by step 3.

  • While using our sample code and tools
    • Client libraries for many different programming languages can help you implement the YouTube Reporting API as well as many other Google APIs.
    • Don't write code from scratch! Our Java, PHP, and Python code samples will help you get started.
    • The APIs Explorer lets you try out sample calls before writing any code.


Cheers,