Wide Angle Intra Prediction Mode Remapping for Image Coding
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images/videos, such as 4K and 8K UHD, leads to higher transmission and storage costs due to increased data amounts, and there is a need for efficient compression techniques, especially for immersive media like VR and AR content.
Innovation Solution
An image coding method that remaps intra prediction modes to a wide angle intra prediction mode based on block sizes, determining a WAIP mode for each separate block size, and using LFNST kernels to derive transform coefficients, enhancing compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image coding methods are used for high-resolution images/videos, then image quality is maintained, but transmission cost and storage cost increase due to increased data amount
Solution Approach 1:
The image block is divided into multiple sub-blocks for separate processing. Different transform kernels are applied to different sub-blocks based on their local characteristics, enabling more efficient compression while maintaining overall image quality.
Solution Approach 2:
Different transform kernels are selected for different sub-blocks based on local image characteristics such as gradient direction and variance. This local adaptation allows optimal compression for each region while preserving important image details.
2Device complexity
If conventional transform methods are used, then processing is simple, but compression efficiency is insufficient for high-resolution and immersive media
Solution Approach 1:
The transform kernel selection is dynamically adapted to local image characteristics. The system automatically selects appropriate kernels based on gradient analysis and variance calculations for each sub-block, enabling adaptive compression without manual configuration.
Solution Approach 2:
Different transform parameters (kernels) are applied to different sub-blocks based on local image statistics. The gradient direction and variance serve as parameters that determine which transform kernel to use, optimizing compression for each region.
Data Source
AI summary
An image decoding method according to the present document may comprise the steps of: when an ISP mode is applied to a current block, remapping an intra prediction mode of the current block to a wide-angle intra prediction mode on the basis of the width and height of a first block or the width and height of a second block; performing prediction on the current block on the basis of the wide-angle intra prediction mode; and remapping the intra prediction mode of the current block to a wide-angle intra prediction mode on the basis of the width and height of the first block or the width and height of the second block, and determining an LFNST set including LFNST matrices on the basis of the wide-angle intra prediction mode, wherein the first block includes a coding block, and the second block includes a partition block.