Cross-Component Sample Offset for Lower Video Bit Rates
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently compressing luma and chroma components due to the lack of effective methods that exploit cross-component relationships, leading to suboptimal coding efficiency and increased bit rates.
Innovation Solution
Implementing a method that determines a sample offset for chroma components based on edge direction and strength of collocated luma samples, using classifiers and quantized edge strengths to modify chroma samples, and applying this method in conjunction with existing in-loop filters to enhance coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional video coding methods are used without exploiting cross-component relationships, then the coding process is simpler, but coding efficiency is suboptimal and bit rates are increased
Solution Approach 1:
The patent merges luma and chroma component processing by using luma edge information to guide chroma sample offset adjustments. The classifier determined from luma samples is applied to chroma components, combining information from both components to improve coding efficiency without treating them completely independently.
Solution Approach 2:
The patent introduces an intermediary classification mechanism that translates luma edge characteristics into chroma offset adjustments. The classifier acts as an intermediary that converts luma gradient information into actionable offset values for chroma samples, enabling cross-component optimization without direct complex interaction.
2Loss of information
If cross-component relationships are exploited to improve coding efficiency, then bit rates are reduced, but the complexity of determining classifiers and sample offsets increases
Solution Approach 1:
The patent applies local quality by determining classifiers and sample offsets for individual chroma samples based on their specific local edge characteristics. Each sample's offset is adjusted according to its own classifier determination from luma edge strength and direction, rather than applying a global offset to all chroma samples uniformly.
Solution Approach 2:
The patent changes parameters by quantizing continuous edge strength values into discrete classifier categories (M segments with M-1 threshold values). This parameter transformation converts continuous gradient information into discrete offset adjustments, reducing the complexity of processing while preserving essential edge information for chroma optimization.
3Measurement precision
If sample offsets are determined based on quantized edge strengths with M segments, then coding precision is improved, but the computational complexity of calculating and applying offsets increases
Solution Approach 1:
The patent applies partial action by using a limited number of discrete segments (M segments) to represent edge strength rather than using the full continuous range. This partial representation captures the most significant edge characteristics while avoiding the computational burden of processing all possible edge strength values, achieving sufficient precision with reduced complexity.
Data Source
AI summary
An electronic apparatus performs a method of decoding video signal, including: receiving, from the video signal, a picture frame that includes a first component, and a second component; selecting an edge direction across a collocated sample of the first component or a current sample of the second component relative to the respective sample of the second component; calculating a first edge strength determining a difference between values of two samples from the group consisting of the collocated or current sample, a first neighbouring sample, and a second neighbouring sample; quantizing the first edge strength into M segments according to M−1 threshold values; determining the classifier for the respective sample of the second component based on the M segments; determining a sample offset for the respective sample of the second component according to the classifier; and modifying the respective sample of the second component based on the determined sample offset.