Adaptive Transform Area Partitioning for Image Coding Efficiency
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Solution Overview
Problem
Conventional image encoding/decoding methods struggle to improve coding efficiency due to the lack of adaptive determination of the transform performing area and partition shape of sub transform blocks.
Innovation Solution
The method involves determining a transform performing area and partitioning it into sub transform blocks using QT and BT partitions, and adaptively selecting a scan method for the sub transform blocks to enhance encoding/decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If transform is performed without determining a performing area separately, then the encoding process is simple, but coding efficiency is limited
Solution Approach 1:
The transform block is divided into multiple sub-transform blocks, and a transform performing area is selectively determined within the transform block based on prediction mode. This segmentation allows the transform to be applied adaptively to specific regions rather than uniformly across the entire block, improving coding efficiency while maintaining manageable processing complexity through structured division.
Solution Approach 2:
The transform performing area is dynamically determined based on the prediction mode (horizontal directional, vertical directional, or other modes). This dynamic adaptation allows the encoding system to adjust the transform processing to match the characteristics of the current block, thereby improving coding efficiency without requiring a completely fixed or static processing approach.
2Productivity
If transform performing area is adaptively determined and sub transform blocks are partitioned, then coding efficiency is improved, but processing complexity increases
Solution Approach 1:
Different transform processing is applied to different regions within the transform block based on the determined performing area. Specifically, sub-transform blocks within the performing area undergo transform processing while others do not, creating local quality variations that match the actual image characteristics and improve encoding efficiency without unnecessarily processing entire blocks.
Solution Approach 2:
The transform processing parameters are changed based on the prediction mode and determined performing area. By adjusting which sub-transform blocks receive transform processing and how they are partitioned (using QT or BT partitions), the system adapts processing parameters to local image characteristics, improving efficiency while keeping the complexity manageable through parameter-based control.
Data Source
AI summary
The present invention provides an image encoding method and an image decoding method. The image decoding method, according to the present invention, comprises the steps of: determining a transformation performing region; segmenting the determined transformation performing region into at least one sub-transform block by using at least one of quadtree segmentation and binary tree segmentation; and performing inverse transformation on the at least one sub-transform block.