Layered Video Quantisation Using Frame Similarity Scores
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Solution Overview
Problem
Existing signal processing systems face inefficiencies in allocating bitrates across video signal time samples, leading to suboptimal visual quality and bitrate utilization, especially as higher definition video becomes more prevalent.
Innovation Solution
The system determines quantisation parameters based on similarity scores derived from comparing pixel values across frames, allowing for dynamic bitrate allocation that prioritizes more influential time samples, thereby optimizing bitrate usage and enhancing visual quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform quantisation parameters are used for all time samples, then device complexity is reduced, but visual quality deteriorates due to inefficient bitrate allocation
Solution Approach 1:
The patent applies local quality by determining different quantisation parameters for different time samples based on their individual similarity scores. Each time sample is evaluated independently, allowing the system to apply appropriate quantisation strength locally rather than uniformly across all frames, thereby optimizing visual quality where it matters most while maintaining manageable complexity.
Solution Approach 2:
The patent implements dynamics by making quantisation parameters adaptive rather than static. The quantisation parameters are dynamically adjusted for each time sample based on calculated similarity scores, allowing the system to respond to actual content characteristics and temporal relationships, improving bitrate allocation efficiency without requiring overly complex predetermined schemes.
2Manufacturing precision
If similarity-based quantisation parameter determination is implemented, then visual quality is improved through optimized bitrate allocation, but device complexity increases due to additional processing requirements
Solution Approach 1:
The patent applies preliminary action by calculating similarity scores between time samples before determining quantisation parameters. This pre-computation of similarity metrics allows the system to make informed decisions about bitrate allocation in advance, enabling optimized visual quality without requiring complex real-time adjustments during the actual quantisation process.
Solution Approach 2:
The patent implements feedback by using calculated similarity scores to inform quantisation parameter selection. The system continuously evaluates temporal relationships between frames and adjusts quantisation parameters accordingly, creating a closed-loop system that optimizes visual quality based on actual content characteristics rather than relying on fixed or overly complex adaptive algorithms.
3Manufacturing precision
If more bitrate is allocated to influential time samples, then visual quality is enhanced, but loss of information increases in less influential time samples
Solution Approach 1:
The patent applies local quality by applying different quantisation strengths to different time samples based on their importance. Influential time samples with high similarity scores receive lower quantisation (preserving more information), while less influential samples receive higher quantisation (accepting more information loss). This localized approach ensures that visual quality is enhanced where it matters most without unnecessarily preserving information in less critical frames.
Solution Approach 2:
The patent implements parameter changes by adjusting quantisation parameters dynamically based on time sample importance. The system changes the quantisation parameter values according to calculated similarity scores, allowing flexible control over the trade-off between preserving information in influential frames and achieving overall visual quality enhancement, rather than using fixed quantisation parameters throughout.
Data Source
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AI summary
A first value of a first data element (311) in a first set of data elements (310) is obtained, the first set of data elements being based on a first time sample of a signal. A second value of a second data element (321) in a second set of data elements (320) is obtained, the second set of data elements being based on a second, later time sample of the signal. A measure of similarity is derived between the first value and the second value. Based on the derived measure, a quantisation parameter useable in performing quantisation on data based on the first time sample of the signal is determined. Output data is generated using the quantisation parameter.