Transform-Skip Residual Decoding for Lower Image Coding Complexity
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images leads to higher transmission and storage costs due to increased image data, necessitating a more efficient image compression technique.
Innovation Solution
A decoding apparatus that derives context-coded bins for context syntax elements, decodes transform coefficients based on these bins, and generates reconstructed pictures, while an encoding apparatus derives residual samples and encodes context syntax elements to improve image compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution and high-quality image data is transmitted, then image quality is improved, but transmission cost and storage cost increase
Solution Approach 1:
The patent segments the image data into blocks and processes them through transform operations to separate frequency components. This allows selective transmission of significant coefficients while discarding negligible ones, maintaining image quality while reducing data volume for transmission.
Solution Approach 2:
The patent extracts and transmits only the essential transform coefficients that carry significant image information. By removing redundant and negligible data components, the system reduces transmission cost while preserving the core image quality through selective reconstruction.
2Loss of energy
If image data is compressed to reduce transmission cost, then transmission efficiency is improved, but compression efficiency is reduced
Solution Approach 1:
The patent changes the parameter representation by transforming spatial domain image data into frequency domain coefficients. This parameter transformation enables more efficient compression by concentrating energy in fewer coefficients, improving both compression efficiency and transmission efficiency simultaneously.
Solution Approach 2:
The patent employs a composite coding approach that combines transform coding with entropy coding. This composite method integrates multiple compression techniques to achieve superior compression efficiency while maintaining low transmission cost, resolving the contradiction between these two objectives.
3Measurement precision
If context syntax elements are decoded using traditional methods, then decoding accuracy is maintained, but decoding complexity increases
Solution Approach 1:
The patent applies partial action by decoding only the necessary context syntax elements required for each specific block, rather than processing all possible elements. This selective decoding approach maintains decoding accuracy for relevant components while significantly reducing overall decoding complexity by avoiding unnecessary processing.
Data Source
AI summary
A decoding apparatus for image decoding includes: a memory and at least one processor connected to the memory, the at least one processor configured to: obtain a transform skip flag of a current block from a bitstream, obtain residual information of the current block from the bitstream based on the transform skip flag, wherein the residual information is residual information on transform skip, derive a specific number of context-coded bins for context syntax elements for the current block, decode the context syntax elements for a current sub-block of the current block included in the residual information based on the specific number, derive transform coefficients for the current sub-block based on the decoded context syntax elements, derive residual samples for the current block based on the transform coefficients, and generate a reconstructed picture based on the residual samples.