Frequency-Dependent Coefficient Sign Coding for Faster Video Compression
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently compressing video data while maintaining video quality, particularly in terms of computational cost and processing time, especially when predicting coefficient signs during transform block coding.
Innovation Solution
The method involves selecting a subset of transform coefficients based on frequency and predicting their signs using context information, reducing computational cost and processing time by performing sign prediction only on select coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sign prediction is performed on all transform coefficients, then video quality is improved, but computational cost and processing time increase
Solution Approach 1:
The patent divides transform coefficients into different frequency groups (low-frequency and high-frequency coefficients) and applies different sign prediction strategies to each group. Low-frequency coefficients use context-based sign prediction while high-frequency coefficients use a different approach, allowing the system to maintain video quality for important low-frequency components while reducing computational cost for high-frequency components.
Solution Approach 2:
The patent applies different sign prediction methods to different frequency regions based on their importance. Low-frequency coefficients, which have greater impact on video quality, receive more sophisticated context-based prediction, while high-frequency coefficients use simpler prediction methods, optimizing the balance between quality and complexity.
2Measurement precision
If sign prediction is performed on all transform coefficients, then video quality is improved, but processing time increases
Solution Approach 1:
The patent segments transform coefficients by frequency and applies selective sign prediction only where necessary. By dividing coefficients into low-frequency and high-frequency groups and applying different prediction strategies, the system reduces the overall processing time while maintaining quality for the most important coefficients.
Solution Approach 2:
The patent applies sign prediction selectively rather than universally. By performing context-based sign prediction only on low-frequency coefficients that have the greatest impact on video quality, and using simpler methods for high-frequency coefficients, the system achieves partial action that optimizes the trade-off between quality and processing time.
3Productivity
If frequency-dependent coefficient sign coding is implemented, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent implements frequency-dependent sign coding by segmenting coefficients into different frequency groups and applying different sign prediction methods to each group. This segmentation approach improves coding efficiency by adapting the prediction method to the frequency characteristics while managing device complexity through systematic classification.
Solution Approach 2:
The patent changes the parameter of sign prediction methodology based on the frequency parameter of transform coefficients. By dynamically selecting different prediction methods based on frequency, the system improves coding efficiency while the structured parameter-based approach helps manage implementation complexity.
Data Source
AI summary
The various embodiments described herein include methods and systems for coding video. In one aspect, a method includes obtaining video data comprising a plurality of blocks, including a first block. The method further includes determining a plurality of transform coefficients associated with the first block; and selecting a subset of the plurality of transform coefficients based on a frequency corresponding to each transform coefficient of the plurality of transform coefficients. The method also includes predicting respective coefficient signs for selected subset of the transform coefficients associated with the first block based on respective context information; and reconstructing the first block based on the plurality of transform coefficients and the predicted respective coefficient signs.