Secondary Transform Image Coding for High-Resolution 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.
Innovation Solution
An image coding method and apparatus utilizing a reduced secondary transform (RST) that changes the array of transform coefficients based on intra prediction mode, enhancing coding efficiency by applying inverse RST in a two-dimensional array from a one-dimensional array.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution and high-quality images/videos are transmitted or stored using conventional methods, then image quality is maintained, but transmission cost and storage cost increase
Solution Approach 1:
The patent applies transform techniques (primary transform and secondary transform) to change the representation parameters of image data from spatial domain to frequency domain, enabling more efficient compression while maintaining reconstruction quality. The transform coefficients allow selective quantization and compression of less important frequency components, reducing transmission and storage costs while preserving perceptual image quality.
2Productivity
If conventional transform methods are used for image coding, then processing is simple, but coding efficiency is insufficient for high-resolution images
Solution Approach 1:
The patent divides the transform processing into multiple stages: a primary transform that processes the entire block, and a secondary transform (including reduced secondary transform) that processes specific regions or coefficients. This segmentation allows the system to achieve high coding efficiency through adaptive processing while managing complexity by applying different transform strengths to different parts of the image data.
Solution Approach 2:
The patent implements dynamic transform selection where the secondary transform is conditionally applied based on prediction mode, block characteristics, and coding conditions. The transform coefficients and processing intensity are adaptively adjusted according to the specific image content and compression requirements, optimizing the balance between coding efficiency and processing complexity.
Data Source
AI summary
An image decoding method according to the present document comprises the steps of: deriving transform coefficients through inverse quantization on the basis of quantized transform coefficients for a target block; deriving modified transform coefficients on the basis of an inverse reduced secondary transform (RST) for the transform coefficients; and generating, on the basis of an inverse primary transform for the modified transform coefficients, a restoration picture based on residual samples for the target block, wherein the modified transform coefficients derived according to the inverse RST are two-dimensionally arranged according to the order of a row priority direction or a column priority direction according to an intra prediction mode to be applied to the target block.