Video Data Compression Using Component Correlation
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Solution Overview
Problem
Current compression and decompression methods for video data face challenges in achieving high compression factors without degrading image quality, while also being complex and energy-intensive, especially in transcoding systems, where they often fail to account for correlations between color components, leading to inefficiencies and errors.
Innovation Solution
A method that selectively compresses and decompresses data blocks by identifying a designated component and using its correlations with other components, allowing for higher compression factors while maintaining image quality, using techniques like predictive compression and error quantification to minimize complexity and loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If compression factor is increased to reduce memory space and energy consumption, then image quality degradation occurs
Solution Approach 1:
The patent merges the compression of multiple color components (Y, Cb, Cr) by exploiting their correlations. Instead of compressing each component independently, the method combines information from multiple components to achieve higher compression ratios while maintaining image quality through joint processing of correlated data.
Solution Approach 2:
The patent transforms the data into a different colorimetric domain (YCbCr with sub-sampling) where correlations between components are more exploitable. This parameter transformation enables more efficient compression by reorganizing how color information is represented and processed.
2Manufacturing precision
If complex compression methods are used to maintain image quality, then processing complexity and energy consumption increase
Solution Approach 1:
The patent segments the compression process into distinct stages: transformation to YCbCr color space, independent compression of the Y luminance component, and differential compression of Cb and Cr chrominance components. This segmentation allows each stage to be optimized independently, reducing overall complexity while maintaining quality.
Solution Approach 2:
The patent applies full compression processing only to the most important Y luminance component, while applying simplified or differential compression to the less critical Cb and Cr chrominance components. This partial application of compression complexity maintains image quality where it matters most while reducing overall processing burden.
3Productivity
If independent compression is applied to each color component, then compression efficiency decreases due to ignoring correlations
Solution Approach 1:
The patent merges the compression of multiple color components (Y, Cb, Cr) by exploiting their correlations. Instead of compressing each component independently, the method combines information from multiple components to achieve higher compression ratios while maintaining image quality through joint processing of correlated data.
Data Source
AI summary
A method for compressing a data block including sets of homologous components may include selecting a designated component from the data block, and compressing non-designated components with a measurable loss less than or equal to a threshold. The method may further include compressing the designated component based upon at least a selection of values from among the values of the homologous designated components associated with the data of the block.