Partial-Block Residual Transformations for Image Decoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing demands for high-resolution and high-quality images result in higher data volumes, leading to increased costs for transmission and storage, necessitating more efficient image encoding/decoding techniques, particularly for stereographic image content.
Innovation Solution
A method and apparatus for encoding/decoding residual data using multiple transformations, including a secondary inverse transformation on a partial region of a block, utilizing a transform matrix and one-dimensional arrangement of residual coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution and high-quality image data is transmitted or stored using conventional methods, then image quality is improved, but transmission and storage costs increase
Solution Approach 1:
The image block is divided into multiple regions, and different transformation techniques are applied to different regions. This segmentation allows the patent to process complex high-resolution images using multiple simpler transformations, improving compression efficiency while maintaining image quality.
Solution Approach 2:
Different transformation methods are applied to different regions of the image block based on local characteristics. The patent performs secondary transformations on specific regions (e.g., regions with large residual values) while using primary transformations on other regions, optimizing compression for each local area.
2Quantity of substance
If conventional image compression techniques are used, then data volume is reduced, but encoding/decoding efficiency needs improvement
Solution Approach 1:
The patent dynamically selects transformation methods and regions based on image characteristics. The encoding process adaptively determines which regions require secondary transformations, making the compression process more efficient and responsive to actual image content rather than applying fixed transformations uniformly.
Solution Approach 2:
The patent changes transformation parameters (such as transformation type and region selection) based on image characteristics. By adjusting these parameters dynamically, the system achieves better compression ratios while maintaining acceptable encoding/decoding speeds.
3Productivity
If secondary transformation is performed on the entire block, then compression efficiency is improved, but device complexity increases
Solution Approach 1:
Instead of applying secondary transformations to the entire block, the patent applies them only to specific regions where they are most beneficial (e.g., regions with large residual values). This partial application reduces computational complexity while still achieving improved compression efficiency where needed.
Solution Approach 2:
The image block is segmented into regions requiring secondary transformation and regions where primary transformation suffices. This segmentation reduces the overall computational burden by limiting complex operations to only the necessary portions of the image.
Data Source
AI summary
An image decoding method according to the present invention can comprise the steps of: acquiring residual coefficients of a current block; dequantizing the residual coefficients; performing secondary inverse transformation on the dequantized residual coefficients; and performing primary inverse transformation on the performance result of the secondary inverse transformation. The secondary inverse transformation can be performed for a partial region of the current block.