Video Residual Quantization Using Temporal Stability Regions
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Solution Overview
Problem
Existing data compression methods, particularly for video signals, struggle to balance compression efficiency with visual quality and processing speed, especially in hierarchical tier-based coding formats, where adaptive quantization is costly and complex.
Innovation Solution
A method is introduced that applies adaptive quantization based on temporal and spatial stability analysis, adjusting quantization step-widths for different areas of a video frame, using temporal information to leverage stable areas and reduce entropy costs, and employing bin folding and quantization offsets to optimize compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If adaptive quantization is applied in hierarchical tier-based coding formats, then compression efficiency is improved, but device complexity and processing cost increase significantly
Solution Approach 1:
The patent divides the video frame into multiple stability regions based on temporal and spatial stability analysis. Each region is independently quantized with its own step-width, allowing adaptive quantization to be applied selectively rather than globally. This segmentation reduces the computational complexity of determining quantization parameters while maintaining compression efficiency benefits in stable regions.
Solution Approach 2:
The patent applies different quantization step-widths to different spatial regions within the same frame based on their stability characteristics. Stable regions (where content changes minimally between frames) receive coarser quantization with larger step-widths, while unstable regions receive finer quantization with smaller step-widths. This local differentiation optimizes compression efficiency without requiring complex adaptive quantization across the entire frame.
2Quantity of substance
If quantization reduces data values to discrete magnitudes, then data compressibility is improved, but visual quality deteriorates
Solution Approach 1:
The patent dynamically adjusts quantization step-widths based on temporal stability analysis. For stable regions where content remains consistent across frames, larger quantization step-widths are applied to achieve higher compression. For unstable regions where content changes significantly, smaller quantization step-widths are used to preserve visual quality. This dynamic adaptation allows the system to optimize the trade-off between compression and quality on a region-by-region basis.
Solution Approach 2:
The patent changes the quantization parameter (step-width) based on the stability characteristic of each region. By analyzing temporal correlations and spatial stability, the system selectively applies different quantization intensities. Stable regions undergo more aggressive quantization with larger step-widths, while unstable regions undergo milder quantization with smaller step-widths, thereby maintaining visual quality where it matters most while achieving overall compression efficiency.
3Quantity of substance
If quantization step-width is increased for compression, then bit rate is reduced, but reconstruction accuracy deteriorates
Solution Approach 1:
The patent segments the video frame into stable and unstable regions, applying different quantization step-widths to each segment. Stable regions use larger step-widths that achieve higher compression with minimal impact on perceived quality, while unstable regions use smaller step-widths to maintain reconstruction accuracy. This segmentation allows the system to achieve overall bit rate reduction without uniformly sacrificing reconstruction accuracy across the entire frame.
Solution Approach 2:
The patent applies aggressive quantization (excessive action) only to stable regions where it has minimal visual impact, while applying milder quantization to unstable regions where accuracy is critical. This partial application of strong compression techniques allows the system to achieve significant bit rate reduction in stable areas without compromising overall reconstruction accuracy in dynamically important areas.
Data Source
AI summary
A method of encoding an input signal is provided, the method comprising: quantizing a set of data based on temporal information associated with said set of data.