Residual Coding with Sub-Block Rice Parameter Adaptation
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Solution Overview
Problem
High-resolution, high-quality image/video data requires efficient compression techniques to reduce transmission and storage costs, especially with the growing demand for VR, AR, and immersive media, as existing methods struggle to effectively compress and transmit such data without increasing costs.
Innovation Solution
A method and device for enhancing image/video coding efficiency by performing a binarization process on residual information based on a rice parameter, with a maximum value of 3, to derive and initialize rice parameters for sub-blocks within current blocks, optimizing residual coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional compression techniques are used for high-resolution image/video data, then transmission and storage costs increase, but image quality and resolution requirements cannot be met
Solution Approach 1:
The current block is divided into multiple sub-blocks for independent residual coding. Each sub-block has its own rice parameter initialization, allowing adaptive compression tailored to local characteristics. This segmentation enables more efficient bit allocation while maintaining high image quality in critical regions.
Solution Approach 2:
Different rice parameters are initialized for different sub-blocks based on local characteristics such as transform coefficient patterns and prediction accuracy. This local adaptation allows the compression scheme to optimize for quality in regions requiring it while achieving higher compression in less critical areas, resolving the contradiction between overall data volume reduction and local quality maintenance.
2Productivity
If residual coding efficiency is improved through adaptive binarization, then compression ratio increases, but computational complexity increases
Solution Approach 1:
The rice parameter initialization is changed from a conventional uniform approach to an adaptive approach based on local block characteristics. By deriving different rice parameters for different sub-blocks based on transform coefficient statistics and prediction residuals, the scheme achieves better compression efficiency without requiring complex external models or iterative optimization.
Solution Approach 2:
Rice parameters are pre-initialized for each sub-block before the actual binarization process based on preliminary analysis of transform coefficient patterns and prediction accuracy. This preliminary action eliminates the need for complex runtime parameter optimization, achieving high compression efficiency with manageable computational complexity.
3Ease of manufacture
If rice parameter maximum value is limited to 3 for binarization, then encoding simplicity increases, but binarization flexibility decreases
Solution Approach 1:
By dividing the current block into multiple sub-blocks and initializing different rice parameters for each, the system compensates for the limited maximum value (3) through increased granularity. Each sub-block can have its own optimized parameter within the 0-3 range, and the collective effect across multiple sub-blocks provides the necessary adaptability for diverse coding scenarios while maintaining encoding simplicity.
Data Source
AI summary
A method for decoding a picture performed by a decoding apparatus according to the present disclosure includes receiving a bitstream including residual information, deriving a quantized transform coefficient for a current block based on the residual information included in the bitstream, deriving a transform coefficient from the quantized transform coefficient based on a dequantization process, deriving a residual sample for the current block by applying an inverse transform to the derived transform coefficient, and generating a reconstructed picture based on the residual sample for the current block.