Transform Matrix Precision for High Bit-Depth Image Encoding
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Solution Overview
Problem
Current video data compression and decompression systems face inefficiencies in entropy encoding, particularly with existing techniques like CABAC, which do not fully optimize bit rate and image quality, especially at higher bit depths and low quantization parameters.
Innovation Solution
The implementation of advanced entropy encoding techniques, including context-adaptive binary arithmetic coding (CABAC) with adaptive context modeling and modified range adjustment, along with fixed-bit encoding schemes to handle higher bit depths, optimizing the bit stream by splitting coefficients into most-significant and least-significant parts for efficient compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If CABAC entropy encoding is used to compress video data, then the bit rate is reduced, but the image quality deteriorates at higher bit depths and low quantization parameters
Solution Approach 1:
The patent applies dynamics by making the entropy encoding process adaptive rather than static. The context modeling is dynamically adjusted based on the actual data characteristics encountered during encoding, allowing the system to optimize between compression efficiency and precision preservation based on local data patterns and quantization parameters
Solution Approach 2:
The patent changes parameters by introducing adaptive context modeling that modifies encoding behavior based on quantization parameter values and bit depth. The system dynamically adjusts encoding parameters to maintain image quality at higher bit depths while still achieving bit rate reduction through entropy encoding
2Productivity
If standard entropy encoding is applied to higher bit depth data, then compression is achieved, but encoding precision is lost
Solution Approach 1:
The system dynamically adapts its encoding precision based on the bit depth of the input data. For higher bit depth data, the context modeling is adjusted to maintain sufficient precision throughout the encoding process, preventing precision loss while still achieving compression through entropy encoding
Solution Approach 2:
The patent modifies encoding parameters specifically for higher bit depth operations, adjusting the context modeling and range adjustment mechanisms to preserve precision. This allows the system to maintain encoding precision proportional to the input data precision while achieving compression
3Quantity of substance
If context adaptive binary arithmetic coding is used, then data size is reduced, but complexity of encoding increases
Solution Approach 1:
The patent implements dynamic context modeling where the complexity of the encoding process is adjusted based on the actual data characteristics. Rather than using a fixed complex model, the system adapts its context modeling to match the local data patterns, achieving compression while managing complexity through adaptive rather than uniformly complex processing
Data Source
AI summary
A method of encoding image data, including: frequency-transforming input image data to generate an array of frequency-transformed input image coefficients by a matrix-multiplication process, according to a maximum dynamic range of the transformed data and using transform matrices having a data precision; and selecting the maximum dynamic range and/or the data precision of the transform matrices according to the bit depth of the input image data.