Adaptive Sample Offset Filtering for Cross-Component Video Decoding
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Solution Overview
Problem
Existing video coding technologies face inefficiencies in intra prediction and motion vector prediction, leading to suboptimal compression ratios and increased bandwidth requirements due to the lack of adaptive filtering techniques that account for local sample characteristics.
Innovation Solution
Implement cross-sample offset filtering with local adaptability by selecting a target sample offset filter based on statistical properties of reconstructed samples, such as edge information and smoothness measures, to enhance intra and inter prediction processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional intra prediction and motion vector prediction are used, then device complexity is reduced, but video coding efficiency deteriorates due to suboptimal compression ratios
Solution Approach 1:
The patent applies local adaptability by selecting different sample offset filters based on statistical properties of reconstructed samples in different regions. The filtering process adapts to local characteristics such as edge information and smoothness measures, allowing different filtering strategies for different areas of the video block, thereby improving coding efficiency without uniformly increasing complexity across the entire system.
Solution Approach 2:
The patent introduces dynamic filter selection where the sample offset filter is chosen based on statistical properties calculated from reconstructed samples. This dynamic adaptation allows the filtering process to respond to local image characteristics, improving compression ratios by optimizing the filtering approach for each specific region rather than using a static filtering method throughout.
2Productivity
If adaptive filtering with local sample characteristics is implemented, then compression ratios are improved, but bandwidth requirements increase due to additional filter parameters
Solution Approach 1:
The patent changes parameters dynamically by selecting different sample offset filters based on statistical properties of the reconstructed samples. Instead of transmitting extensive filter parameter data, the system adapts filter selection based on calculated statistical measures from the video content itself, improving compression ratios while minimizing additional bandwidth requirements through intelligent parameter adaptation rather than explicit parameter transmission.
3Manufacturing precision
If non-linear mapping for sample offset is used, then manufacturing precision of filtered samples is improved, but device complexity increases due to additional processing steps
Solution Approach 1:
The patent performs preliminary calculations of statistical properties from reconstructed samples before selecting the appropriate sample offset filter. This preliminary action enables the system to determine the optimal filtering approach in advance, improving the accuracy of filtered samples through non-linear mapping while managing processing complexity by organizing computations in a structured sequence rather than requiring complex simultaneous processing.
Data Source
AI summary
A method for in-loop sample offset filtering in a video decoder is disclosed. The method includes obtaining at least one statistical property associated with reconstructed samples of at least a first color component in a current reconstructed data block of a video stream, selecting a target sample offset filter among a plurality of sample offset filters based on the at least one statistical property, the target sample offset filter comprising a nonlinear mapping between sample delta measures and sample offset values, and filtering a current sample in a second color component of the current reconstructed data block using the target sample offset filter and reference samples in a third color component of the current reconstructed data block to generate a filtered reconstructed sample of the current sample.