Adaptive Non-Linear Sample Offset Filtering for Video Compression
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently reducing redundancy and distortion in video signals, particularly in intra and inter prediction methods, leading to suboptimal compression ratios and quality.
Innovation Solution
Implementing adaptive non-linear sample offset filtering in video encoding and decoding, utilizing statistical properties of reconstructed samples to select appropriate filters for cross and local sample offset filtering, enhancing intra and inter prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional intra/inter prediction methods are used, then video coding can be performed with standard algorithms, but compression efficiency and quality are suboptimal
Solution Approach 1:
The patent applies local adaptivity by dividing the image into blocks and selecting different sample offset filter types (first sample offset filter for smooth regions, second sample offset filter for edge regions) based on local characteristics. This allows optimization of compression efficiency and quality for each local region rather than applying a uniform filter across the entire image.
Solution Approach 2:
The patent implements dynamic adaptation by determining the appropriate sample offset filter type based on statistical properties of reconstructed samples (such as variance or gradient metrics) that vary across different image regions. This dynamic selection enables the system to adapt to changing local characteristics and achieve optimal compression performance.
2Productivity
If higher compression ratios are applied, then bandwidth and storage requirements are reduced, but distortion and information loss increase
Solution Approach 1:
The patent reduces information loss by applying different filtering strategies to different regions. Smooth regions can tolerate higher compression with simpler filters, while edge regions use more sophisticated filters to preserve critical visual information, thereby maintaining quality at higher compression ratios.
Solution Approach 2:
The patent uses feedback from statistical properties of reconstructed samples to guide the filtering process. By analyzing variance and gradient information, the system can adaptively adjust the sample offset filtering to minimize distortion while maximizing compression efficiency.
3Device complexity
If sample offset filtering is applied to all regions uniformly, then processing is simplified, but compression efficiency and quality optimization are limited
Solution Approach 1:
The patent balances complexity and efficiency by implementing a systematic approach to local adaptivity. The image is divided into manageable blocks, and statistical properties are computed to determine filter types, providing structured complexity that achieves superior compression efficiency compared to uniform filtering.
Solution Approach 2:
The patent optimizes compression efficiency by changing filtering parameters (selecting different sample offset filter types) based on local statistical properties. This parameter adaptation allows the system to achieve high compression efficiency while keeping the processing algorithm relatively simple and systematic.
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.