Context-Coded Transform Kernel Signaling for Image Compression
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Solution Overview
Problem
The increasing demand for high-resolution, high-quality images and immersive media formats like VR and AR necessitates more efficient image/video compression techniques to reduce transmission and storage costs, as existing methods struggle with the higher data requirements.
Innovation Solution
A method for signaling information indicating a transform kernel set in image coding, including context coding or bypass coding of bin strings for MTS index, to enhance image/video coding efficiency and lower coding complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image/video compression techniques are used for high-resolution, high-quality images, then transmission and storage costs increase, but image quality and resolution requirements cannot be met
Solution Approach 1:
The patent applies multiple transform kernels (different from conventional single kernel) to transform residual signals in image coding. By changing the transform parameters and selecting appropriate kernels based on block characteristics, the compression efficiency is improved, allowing high-resolution images to be compressed more effectively, thus reducing transmission and storage costs while maintaining image quality.
2Productivity
If multiple transform kernels are used to improve compression efficiency, then coding complexity increases, but if conventional single kernel is used, then compression efficiency is insufficient
Solution Approach 1:
The patent divides the image into multiple blocks and applies different transform kernels to different blocks based on their characteristics. This segmentation approach allows the system to use multiple transform kernels to improve overall compression efficiency while managing coding complexity by processing each block independently with the most suitable kernel.
Solution Approach 2:
The patent dynamically selects transform kernels based on block characteristics such as texture and content type. This dynamic adaptation allows the coding system to optimize compression efficiency for each block while avoiding the complexity of using all possible kernels for all blocks, thus balancing compression efficiency and coding complexity.
Data Source
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AI summary
An image decoding method according to the present document comprises a step of generating residual samples of a current block on the basis of residual information, wherein the residual information comprises a multiple transform selection (MTS) index and information regarding transform coefficients, the residual samples are generated from transform coefficients according to the information regarding the transform coefficients by using a transform kernel set, the transform kernel set is determined by the MTS index from among transform kernel set candidates, at least one of bins of a bin string of the MTS index is derived on the basis of context coding, the context coding is performed based on a value of a context index with respect to the MTS index.