Lossless Video Coding Sub-Frame Quantization
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Solution Overview
Problem
Current lossless video encoding techniques face limitations in achieving high compression ratios due to information loss during the transformation and quantization of residual blocks, which results in a lossy decoding process that differs from the original data.
Innovation Solution
The method involves selecting an optimal quantization value for each block to minimize the number of bits in the encoded residual and difference blocks, and including the encoded difference block in the bitstream to recreate the original residual block during decoding, thereby achieving lossless encoding with improved compression ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lossy compression techniques are used to reduce data size, then compression ratio is improved, but decoding accuracy deteriorates
Solution Approach 1:
The patent segments the residual block into multiple sub-blocks and performs separate quantization for each sub-block. This allows different quantization values to be applied to different regions, optimizing the balance between compression and accuracy for each sub-region independently, thereby achieving better overall compression ratio while maintaining decoding precision.
Solution Approach 2:
The patent applies different quantization values to different sub-blocks based on local characteristics. By identifying the optimal quantization value for each sub-block individually, the encoding process adapts to local variations in the residual data, achieving superior compression efficiency without sacrificing reconstruction accuracy in critical regions.
2Productivity
If quantization is applied to residual blocks to reduce bits, then compression ratio is improved, but information loss increases
Solution Approach 1:
The patent dynamically determines optimal quantization values for each sub-block based on the actual residual data characteristics. Rather than using a fixed quantization scheme, the method adapts the quantization parameters to match the local properties of each sub-block, maximizing compression ratio while minimizing information loss through data-driven parameter selection.
Solution Approach 2:
The patent changes the quantization parameter (quantization value) for each sub-block to optimize the balance between compression and information preservation. By systematically searching and selecting the optimal quantization value that minimizes the number of bits while maintaining reconstruction accuracy, the method achieves high compression ratio without excessive information loss.
3Productivity
If optimal quantization values are selected for each block to minimize bits, then compression efficiency is improved, but encoding complexity increases
Solution Approach 1:
The patent divides the block into sub-blocks and performs independent quantization optimization for each sub-block. This segmentation allows the encoding process to achieve better compression efficiency through localized optimization while managing complexity by processing smaller, independent units rather than optimizing the entire block at once.
Solution Approach 2:
The patent applies quantization optimization to sub-blocks rather than requiring full optimization of the entire block. This partial action approach achieves sufficient compression efficiency improvement without the excessive computational complexity that would result from optimizing every possible parameter across the complete block, representing a practical trade-off that delivers adequate performance with manageable complexity.
Data Source
AI summary
A block of a frame of a video stream can be encoded using lossless coding that generates a transform domain residual block and a spatial domain difference block. The compression ratio of the coding may be improved by selecting an optimal quantization value on a per-block basis. The optimal quantization value can be selected by quantizing a residual block resulting from prediction of the block using a plurality of candidate quantization values and selecting the candidate quantization value that results in the fewest number of bits for the quantized residual block.