Pixel Data Stream Video Compression Method
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Solution Overview
Problem
Current data compression techniques for sampled data, such as aperture sampling, do not provide sufficient compression for the increasing amounts of media content generated by electronic devices, leading to excessive storage space consumption and increased bandwidth and transmission time requirements.
Innovation Solution
The method involves compressing video data by extracting pixel data streams from each pixel in a video frame and applying data compression to each stream individually, rather than compressing entire frames, using algorithms that discard data points based on allowable change thresholds or beam narrowing techniques to achieve lossless or lossy compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional frame-based video compression is used, then compression is applied to entire frames, but storage space and transmission bandwidth are still excessive
Solution Approach 1:
The video data is segmented into individual pixel data streams, with each pixel's values across multiple frames forming a separate stream. This segmentation allows independent compression of each pixel stream, enabling more efficient data reduction compared to traditional frame-based compression that processes all pixels in a frame together.
2Productivity
If aperture sampling compression is applied, then significant changes in signal amplitude are captured, but sufficient compression is not achieved for increasing media content
Solution Approach 1:
The invention changes the fundamental parameter of compression from frame-based processing to pixel-time series processing. By treating each pixel's temporal sequence as an independent data stream and applying compression algorithms to these streams, the system achieves higher compression ratios that can keep pace with increasing media content volumes.
3Quantity of substance
If individual pixel data streams are compressed, then storage space is reduced, but data processing complexity increases
Solution Approach 1:
By dividing the video data into independent pixel streams, the compression process can be parallelized across multiple processors or processing units. Each pixel stream is processed independently, which actually simplifies the processing architecture compared to frame-based methods that must handle complex inter-pixel relationships within each frame.
Data Source
AI summary
A computer-implemented method for compressing video data comprises receiving a sequence of video data values, each video data value being a digital value from a successive one of a plurality of pixels that form a video sensor, the sequence of video data values resulting from successive frames of video captured by the video sensor; extracting the video data values for each pixel in turn to create a plurality of pixel data streams, each pixel data stream including the video data value for each frame of captured video for the pixel; and applying data compression to each pixel data stream to create compressed data for each pixel data stream.