Streaming video with internally-variable frame quality based on intelligent identification of high-relevance regions

A computing system, comprising:

• a processor;
• and
• a computer-readable storage medium operatively coupled to the processor, the computer-readable storage medium comprising computer-executable instructions that, when executed by the processor, cause the processor to:
• access a video comprising a sequence of video frames;
• evenly sample at least a first video frame in the sequence of video frames and a second video frame in the sequence of video frames, wherein unsampled intermediate video frame(s) are positioned between the first sampled video frame and the second sampled video frame in the sequence of video frames;
• analyze regions of the first sampled video frame using a convolutional neural network (CNN) model to identify a high-relevance region comprising textual information, the high-relevance region being more relevant to a user than low-relevance region(s) within the first sampled video frame;
• extract coordinates of the high-relevance region within the first sampled video frame using the CNN model;
• using the coordinates extracted from the first sampled video frame to identify corresponding high-relevance region(s) within the unsampled intermediate video frame(s), wherein the high-relevance region within the first sampled video frame and the corresponding high-relevance region(s) of the unsampled intermediate video frame(s) have identical coordinates;
• and
• stream the video based on the extracted coordinates such that the streamed video has an internally-variable frame quality, wherein the processor streams the video based on the extracted coordinates by buffering the high-relevance region of the first sampled video frame and the corresponding high-relevance region(s) of the unsampled intermediate video frame(s) at a higher resolution than the low-relevance region(s) of the first sampled video frame.

A computer-readable storage medium comprising computer-executable instructions that, when executed by a processor, cause the processor to:

• access a video comprising a sequence of video frames;
• evenly sample at least a first video frame in the sequence of video frames and a second video frame in the sequence of video frames, wherein unsampled intermediate video frame(s), positioned between the first sampled video frame and the second sampled video frame in the sequence of video frames;
• analyze regions of the first sampled video frame using a convolutional neural network (CNN) model to identify a high-relevance region comprising textual information, the high-relevance region being more relevant to a user than low-relevance region(s) within the first sampled video frame;
• extract coordinates of the high-relevance region within the first sampled video frame using the CNN model;
• use the coordinates extracted from the first sampled video frame to identify corresponding high-relevance region(s) within the unsampled intermediate video frame(s), wherein the high-relevance region within the first sampled video frame and the corresponding high-relevance region(s) of the unsampled intermediate video frame(s) have identical coordinates;
• and
• stream the video based on the extracted coordinates such that the streamed video has an internally-variable frame quality, wherein the processor streams the video by buffering the high-relevance region of the first sampled video frame and the corresponding high-relevance region(s) of the unsampled intermediate video frame(s) at a higher resolution than the low-relevance region(s) of the first sampled video frame.