Cross-Component In-Loop Filtering for Lower-Complexity Video Decoding
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images leads to higher data volumes, increasing transmission and storage costs, necessitating improved video encoding/decoding techniques.
Innovation Solution
Adaptive in-loop filtering methods, including cross-component adaptive loop filtering, are employed to optimize video encoding/decoding processes, reducing computational complexity and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high resolution and quality images are used, then image quality is improved, but data volume increases leading to higher transmission and storage costs
Solution Approach 1:
The image processing is divided into multiple stages: prediction, transform, quantization, and in-loop filtering. Each stage processes specific components (luma and chroma separately) with different precision levels, allowing high quality output while managing data volume through selective processing.
Solution Approach 2:
Different filtering operations are applied to different regions and components: luma blocks receive adaptive loop filtering, chroma blocks receive cross-component adaptive loop filtering using luma gradients, and deblocking filters are applied at block boundaries. This localized quality enhancement maintains overall image quality while reducing global data processing requirements.
2Quantity of substance
If conventional video compression techniques are used, then data compression is achieved, but in-loop filtering increases computational complexity and memory access bandwidth
Solution Approach 1:
The filtering process is segmented into distinct operations: deblocking filter for block boundaries, sample adaptive offset for region-based corrections, and adaptive loop filtering for detailed enhancement. Each segment operates on specific data with dedicated algorithms, reducing overall computational complexity compared to a unified filtering approach.
Solution Approach 2:
Filtering parameters such as filter strength, offset values, and adaptive parameters are dynamically adjusted based on local image characteristics (gradients, variance, block boundaries). This parameter adaptation allows effective filtering while avoiding unnecessary computations in regions where strong filtering is not needed, thus reducing computational complexity.
Data Source
AI summary
The present invention relates to a video decoding method, according to embodiment of the invention, image decoding method comprises, acquiring a reconstructed luma block of a current block, determining whether a cross-component adaptive loop filter is applied to the current block, acquiring a reconstructed chroma block of the current block, to which an adaptive loop filter is applied, when the cross-component adaptive loop filter is applied to the current block and acquiring a final reconstructed chroma block of the current block, to which the cross-component loop filter is applied, using the reconstructed luma block and the reconstructed chroma block, to which the adaptive loop filter is applied.