Residual Data Encoding and Decoding With Partial-Block Transforms
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Solution Overview
Problem
The increasing resolution and quality of high-definition and ultra-high-definition 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 primary and secondary inverse transformation on partial regions of blocks, utilizing a transform matrix and one-dimensional matrix arrangement for improved efficiency.
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 maintained, but transmission and storage costs increase
Solution Approach 1:
The image block is divided into multiple regions, and different transform types are applied to different regions. This segmentation allows selective application of transformation operations, improving compression efficiency while maintaining image quality in critical areas.
Solution Approach 2:
Different transform types are applied to different regions within a block based on local characteristics. This local quality approach ensures that regions with different features (e.g., smooth vs. textured areas) are processed appropriately, optimizing both compression and quality.
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 transform type is dynamically selected for different regions based on local image characteristics. This dynamic adaptation allows the encoding process to optimize compression efficiency while maintaining productivity through automated region-based processing.
Solution Approach 2:
Different transform parameters are applied to different regions of the image block. By changing transform parameters locally rather than uniformly, the system achieves better compression ratios without significantly increasing computational complexity.
3Device complexity
If a single transform type is applied to the entire block, then processing is simple, but compression efficiency is limited
Solution Approach 1:
The block is segmented into multiple regions, each processed with appropriate transform types. This segmentation improves compression efficiency by adapting to local characteristics while keeping individual region processing relatively simple.
Solution Approach 2:
Multiple transform types are made available for selection within the same processing framework. This multi-functionality allows the system to handle diverse image characteristics using a unified processing structure, balancing complexity and efficiency.
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.