Adaptive Loop Filtering for Lower-Complexity Image Decoding
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Solution Overview
Problem
The increasing demand for high-resolution, high-quality images such as HD or UHD images leads to higher data volumes, resulting in increased costs for transmission and storage. Existing image compression techniques, including inter prediction, intra prediction, and entropy encoding, while efficient, do not fully address the complexity and efficiency requirements for advanced image encoding and decoding processes.
Innovation Solution
An adaptive loop filtering method and apparatus that enhances image encoding and decoding efficiency by applying an adaptive loop filter with a fixed filter shape to reconstructed blocks. The filter coefficients are allocated in a central symmetrical manner based on the filter shape, allowing for efficient filtering of both luma and chroma components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution, high-quality image compression techniques are adopted, then image quality and resolution are improved, but transmission and storage costs increase due to higher information bits
Solution Approach 1:
The patent applies adaptive loop filtering with dynamically adjustable filter coefficients to reconstruct image blocks, changing the parameter of filter strength adaptively based on local image characteristics to maintain high quality while reducing bit requirements
Solution Approach 2:
The filtering module performs multiple functions: it reduces blocking artifacts, smooths noise, and reconstructs image blocks, thereby achieving high image quality through a single multi-functional process that reduces overall compression complexity
2Quantity of substance
If complex image compression techniques are used to reduce data size, then transmission and storage costs decrease, but encoding and decoding complexity increases
Solution Approach 1:
The patent performs preliminary filtering operations during the encoding phase to pre-process image blocks before transformation and quantization, reducing the complexity of subsequent decoding operations and overall system complexity
Solution Approach 2:
The adaptive loop filter uses the reconstructed image blocks themselves as reference for filtering operations, allowing the system to self-adjust and reduce complexity without requiring external complex control mechanisms
3Measurement precision
If adaptive filtering with variable filter shapes is applied, then filtering accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent implements dynamic filter coefficient adjustment based on local image characteristics such as edge strength and texture complexity, allowing the filter to adapt its strength dynamically while maintaining a fixed filter shape to balance accuracy and complexity
Solution Approach 2:
The filtering operation applies different filter coefficients to different local regions of the image based on local characteristics, achieving high filtering accuracy in edge and texture regions while using simpler filtering in smooth regions, thereby balancing accuracy and computational complexity
Data Source
AI summary
A method for image decoding, according to the present invention, includes the following steps: receiving image information including a plurality of filter coefficients; generating a restored block for a current block on the basis of the image information; and applying an adaptive loop filter to the restored block on the basis of the plurality of filter coefficients. According to the present invention, image encoding efficiency may be improved, and complexity may be reduced.