Temporal Quantisation Control for Efficient Video Bitrate Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing signal processing systems face challenges in efficiently managing bitrate allocation across time samples of signals, leading to inefficient use of bandwidth and potential losses in visual quality, especially with the increasing demand for higher quality, higher definition video.
Innovation Solution
The system determines quantisation parameters based on a measure of similarity between data elements from different time samples, allowing for dynamic and intelligent bitrate allocation that prioritizes more influential time samples, thereby improving visual quality and reducing bitrate inefficiencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If quantisation is applied to compress signals, then data amount is reduced, but signal quality deteriorates
Solution Approach 1:
The patent applies dynamic quantisation parameter adjustment based on temporal similarity measures. The system continuously evaluates similarity between current and previous time samples, and adaptively modifies quantisation parameters in real-time to optimize the balance between compression ratio and signal quality, rather than using fixed quantisation parameters throughout the signal processing.
Solution Approach 2:
The patent changes quantisation parameters based on derived similarity measures between time samples. When temporal similarity is high, more aggressive quantisation is applied; when similarity is low, less aggressive quantisation is used to preserve quality. This parameter adaptation resolves the contradiction by making quantisation intensity dependent on local signal characteristics.
2Manufacturing precision
If scalable encoding techniques are used to maintain quality, then information storage requirements increase
Solution Approach 1:
The patent applies different quantisation strengths to different time samples based on their temporal similarity characteristics. Time samples with high similarity to previous frames receive stronger compression, while those with low similarity receive weaker compression to preserve quality. This local adaptation eliminates the need for uniform high-quality encoding across all samples.
Solution Approach 2:
The patent applies quantisation selectively based on temporal redundancy analysis. Rather than applying full quantisation to all samples, the system identifies and applies quantisation only where temporal similarity indicates redundancy exists, performing partial action only where beneficial for compression without sacrificing necessary quality.
3Ease of operation
If uniform quantisation is applied to all time samples, then processing is simple, but bitrate allocation is inefficient
Solution Approach 1:
The patent introduces dynamic quantisation parameter adjustment based on temporal similarity measures. The system continuously evaluates similarity between current and previous time samples, and adaptively modifies quantisation parameters in real-time to optimize the balance between compression ratio and signal quality, rather than using fixed quantisation parameters throughout the signal processing.
Data Source
AI summary
A first value of a first data element in a first set of data elements 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 in a second set of data elements 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.