Image Coding With Adaptive Reduced Secondary Transforms for Compression
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images/videos, particularly in immersive media formats like VR and AR, necessitates a more efficient image/video compression technique to reduce transmission and storage costs while maintaining quality.
Innovation Solution
An image coding method based on a reduced secondary transform (RST) that optimizes the transformation kernel matrix and adjusts the array of transform coefficients according to the intra prediction mode, enhancing coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution and high-quality image/video data is transmitted or stored, then image quality is improved, but transmission cost and storage cost increase
Solution Approach 1:
The patent applies parameter changes by transforming image data from spatial domain to frequency domain using transform kernels, and by adjusting quantization parameters to optimize the balance between image quality and data compression ratio, thereby reducing transmission and storage costs while maintaining quality
Solution Approach 2:
The patent extracts and transmits only the most significant transform coefficients after transformation, discarding or coarsely encoding less important coefficients, which reduces the amount of data that needs to be transmitted and stored while preserving essential image quality
2Productivity
If conventional transform methods are used for compression, then coding simplicity is maintained, but compression efficiency is insufficient for high-resolution content
Solution Approach 1:
The patent segments the transform process into multiple stages including primary transform, secondary transform, and quantization, with each stage handling specific aspects of the compression task, allowing for optimized processing at each level while achieving overall high compression efficiency
Solution Approach 2:
The patent introduces dynamic adaptability by selecting different transform kernels and secondary transform types based on prediction modes and block characteristics, allowing the system to adapt to different content types and achieve optimal compression efficiency for various scenarios
Data Source
AI summary
An image decoding method according to the present specification comprises the steps of: deriving transform coefficients through inverse quantization on the basis of quantized transform coefficient for a target block; deriving modified transform coefficients on the basis of inverse reduced secondary transform (RST) of the transform coefficients; and generating a reconstructed picture on the basis of residual samples for the target block on the basis of an inverse primary transform of the modified transform coefficients, wherein the step of deriving the modified transform coefficients is characterized in deriving 16 modified transform coefficients by applying a transform kernel matrix to 8 transform coefficients in a 4×4 region of the target block.