Codebook-Based Quality Restoration Filtering for Video Coding
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Solution Overview
Problem
Current video compression techniques, such as those in H.264 and H.265, face challenges with high computational complexity and bit cost due to the need for iterative computation of Wiener filters and transmission of numerous filter coefficients, which results in inefficiencies and limited gains in compression efficiency.
Innovation Solution
The implementation of a codebook-based approach for quality-restoration filtering, where only an index to a filter stored in the codebook is transmitted, reducing computational complexity and bit cost by avoiding the need for simultaneous computation of multiple filter sizes and types, and allowing for adaptive filter selection based on content-dependent processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative computation of Wiener filters is performed to achieve optimal filtering, then filtering quality is improved, but computational complexity increases
Solution Approach 1:
The patent pre-computes and stores optimal filter coefficients for various video content types and impairment conditions in a codebook during an offline training phase. During actual video encoding/decoding, the system only needs to lookup and apply the pre-computed coefficients rather than performing iterative Wiener filter computations, thus achieving optimal filtering quality with minimal computational complexity.
Solution Approach 2:
The system prepares multiple sets of filter coefficients in advance, each optimized for specific video characteristics (e.g., natural video, screen content, different impairment types). This pre-preparation cushions against the computational burden that would otherwise be required during real-time processing, allowing the system to handle various scenarios without performing complex iterative computations on-the-fly.
2Adaptability or versatility
If multiple filter sizes and types are computed simultaneously to handle various video content, then adaptability is improved, but bit cost increases
Solution Approach 1:
The system pre-generates a comprehensive codebook containing multiple filter coefficient sets corresponding to different filter sizes and types during an offline training phase. Each coefficient set is labeled or categorized by its optimal usage scenario. During video processing, the system only transmits a compact index or identifier pointing to the appropriate pre-computed coefficient set, rather than transmitting all possible filter data, thus achieving high adaptability with minimal bit cost.
Solution Approach 2:
Instead of computing and transmitting multiple complete filter sets, the system creates a compact reference (index) that copies or points to the appropriate filter coefficients in the shared codebook. Both encoder and decoder maintain identical codebooks, allowing the decoder to retrieve the correct filter coefficients using only the transmitted index, significantly reducing the bit cost while maintaining full adaptability.
3Productivity
If content-dependent adaptive filtering is implemented to improve compression efficiency, then compression efficiency is improved, but device complexity increases
Solution Approach 1:
The system performs content analysis and optimal filter selection during an offline training phase, pre-determining the best filter coefficients for various video content types and impairment conditions. These pre-determined coefficients are stored in a codebook with metadata indicating their optimal usage scenarios. During actual video encoding/decoding, the system only needs to match the current video content characteristics against the pre-computed categories and apply the corresponding filter, dramatically reducing processing complexity while maintaining high compression efficiency.
Solution Approach 2:
The system pre-computes filter coefficients for multiple different parameter configurations (different filter sizes, different video content types, different impairment conditions) and stores them in the codebook. During video processing, the system changes only the selected parameter set (by choosing the appropriate codebook entry) rather than re-computing filters, enabling content-dependent adaptive filtering with minimal processing complexity.
Data Source
AI summary
A system and method for quality restoration filtering is described that can be used either in conjunction with video coding, or standalone for postprocessing. It uses wiener filtering approach in conjunction with an efficient codebook representation.