Generalized Filter for Video Compression Artifact Removal
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Solution Overview
Problem
Current video coding technologies face challenges in effectively correcting artifacts introduced during video compression, particularly in filtering operations at both video encoders and decoders.
Innovation Solution
A generalized filter function is applied to pixels in a video frame, allowing for various regions of support and using real-valued coefficients to modify target samples based on neighboring values, with options for linear and non-linear functions, enabling in-loop or post-filtering capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional filtering operations are used in video coding, then artifacts during video compression can be corrected, but the filtering capability is limited and not flexible enough to handle various artifact types effectively
Solution Approach 1:
The patent implements a universal filter function that can perform multiple filtering operations (deblocking, de-ringing, smoothing) through a single unified framework. The filter function accepts different parameter configurations to adapt to various artifact types, making the system versatile while maintaining reliable artifact correction across different video compression scenarios
Solution Approach 2:
The filter parameters (beta_offset, tau_offset, filtering strength) are dynamically adjusted based on the strength of compression artifacts detected in the video data. This dynamic adaptation allows the filter to effectively handle varying artifact severity levels, improving both versatility and reliability in correcting different types of compression artifacts
2Manufacturing precision
If filtering strength is increased to better correct artifacts, then video quality improves, but processing complexity and computational load increase
Solution Approach 1:
The patent changes the parameters of the filter function (beta_offset, tau_offset, clipping thresholds) to control filtering strength. By adjusting these parameters, the system can achieve high video quality with strong artifact correction while managing computational complexity through optimized parameter selection and conditional filtering application
3Manufacturing precision
If strong filtering is applied to correct severe artifacts, then video quality improves, but natural details and edges may be lost
Solution Approach 1:
The filter function uses feedback mechanisms where the filtering operation is applied iteratively or adaptively based on the detected artifact characteristics. The filter strength and application regions are adjusted based on local video content analysis, preserving natural details and edges while correcting artifacts in appropriate regions
Solution Approach 2:
The patent applies filtering with locally adapted parameters differentiating between regions with compression artifacts and regions with natural video details. By analyzing local characteristics and applying filtering only where needed with appropriate strength, the system improves video quality without losing natural video information in non-artifact regions
Data Source
AI summary
A target sample x(i,j) of a two-dimensional array of reconstructed samples is filtered based on values of samples in a neighboring region of the target sample to produce a two-dimensional array of modified reconstructed samples, according to the equation: y(i,j)=round(x(i,j)+g(Σm,n∈Ra(m,n)ƒ(x(i,j)−b(m,n)×(m,n)))), where y(i,j) is a modified target sample value, R is the neighboring region of the target sample, a(m,n) and b(m,n) are real-valued coefficients, round(x) is a function that maps the value x to an integer value in the range [0,2B−1], B is the number of bits representing each sample of the two-dimensional array of modified reconstructed samples, f(x) and g(x) are functions, wherein (a) f(x) is a non-linear function, or (b) g(x) is a non-linear function and both a width and a height of R is more than one sample.