Adaptive In-Loop Filtering for Video Distortion Reduction
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Solution Overview
Problem
Current video encoding/decoding techniques face challenges in minimizing distortion between original and reconstructed pictures, particularly with high-resolution videos, as existing filtering methods like deblocking and sample adaptive offset (SAO) are limited in reducing artifacts and optimizing rate-distortion.
Innovation Solution
The implementation of an adaptive in-loop filtering method that uses adaptive parameter sets, including chroma and luma adaptive in-loop filters, to filter coding tree blocks based on specific filtering criteria, such as clipping flags and indices, to reduce distortion and improve video encoding/decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If deblocking filtering is performed on block boundaries, then blocking artifacts are reduced, but distortion between original and reconstructed pictures cannot be minimized
Solution Approach 1:
The patent applies different filtering strategies to different regions (luma and chroma components) based on their specific characteristics. Luma components receive one type of filtering while chroma components receive another, allowing each region to be optimized for its specific artifact reduction needs while minimizing overall distortion.
Solution Approach 2:
The patent introduces adaptive parameters including clipping flags and indices that dynamically adjust filtering strength and characteristics based on local image characteristics. This allows the filtering process to adapt to different content types and regions, minimizing distortion while reducing artifacts.
2Manufacturing precision
If sample adaptive offset is applied to reduce ringing artifacts, then distortion is reduced to some degree, but there is a limit when the difference between original and reconstructed picture is large
Solution Approach 1:
The patent implements dynamic adaptive in-loop filtering that adjusts filtering parameters based on the actual difference between original and reconstructed pictures. When differences are large, the system automatically strengthens filtering; when differences are small, it reduces filtering intensity, maintaining adaptability across various quality scenarios.
Solution Approach 2:
The filtering process incorporates feedback mechanisms that continuously monitor the reconstructed picture quality and adjust filtering parameters accordingly. This feedback loop enables the system to optimize distortion reduction in real-time based on actual image content and reconstruction accuracy.
3Manufacturing precision
If high-resolution, high-quality video is transmitted or stored, then video quality is improved, but transmission or storage cost increases
Solution Approach 1:
The patent optimizes the balance between video quality and data量 by dynamically adjusting filtering parameters and compression settings. This allows high-quality video transmission at reduced data rates, lowering storage and transmission costs while maintaining acceptable quality levels.
Data Source
AI summary
Disclosed herein is a video decoding method including acquiring adaptation parameter sets including an adaptive in-loop filter (ALF) set including a plurality of ALFs, determining an adaptation parameter set applied to a current picture or slice and including an ALF set applied to the current picture or slice, from among the adaptive parameter sets, determining an adaptation parameter set applied to a current coding tree block (CTB) and including an ALF set applied to the current CTB included in the current picture or slice, from the adaptation parameter set applied to the current picture or slice, and filtering the current CTB based on the ALF set of the determined adaptation parameter set applied to the current CTB, wherein the acquired adaptation parameter sets include chroma ALF number information, and wherein the ALF set includes chroma ALFs, the number of which is indicated by the chroma ALF number information.