Video Quantization Modulation via Luminance Chrominance Saturation
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Solution Overview
Problem
Existing video compression techniques often result in non-constant image quality due to inaccurate representation of macro-blocks, especially when increasing quantizer values, which can distort frequencies, textures, and colors sensitive to the human eye.
Innovation Solution
The method involves determining luminance and chrominance saturation thresholds for each macro-block or sub-block within a frame, using average (d.c.) values of luminance and chrominance components, to modulate the quantization factor, thereby maintaining image quality without affecting the bit rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a higher quantizer value is used to reduce bit rate, then data compression is improved, but image quality deteriorates
Solution Approach 1:
The patent applies different quantizer values to different macro-blocks within the same image frame. High-quantizer values are applied to macro-blocks with saturated colors or high luminance variance where human visual sensitivity is lower, while low-quantizer values are applied to macro-blocks with saturated colors or high chrominance variance where human visual sensitivity is higher. This local adaptation allows aggressive compression in less sensitive regions while preserving quality in more sensitive regions.
Solution Approach 2:
The patent dynamically adjusts quantizer values based on the statistical properties of each macro-block, specifically calculating luminance saturation and chrominance saturation metrics. The quantizer selection is made adaptive and variable rather than fixed, allowing the compression system to respond to local image characteristics and optimize the balance between bit rate and quality for each region.
2Device complexity
If uniform quantization is applied across the entire image, then processing simplicity is maintained, but non-constant quality is produced
Solution Approach 1:
The patent divides the image into macro-blocks and calculates saturation metrics for each block to determine appropriate quantizer values. This local quality approach ensures that each macro-block receives customized quantization parameters based on its specific color and luminance characteristics, producing constant perceived quality across the entire image while maintaining manageable processing complexity through automated statistical analysis.
Data Source
AI summary
Embodiments are directed towards modifying a quality of an image without affecting a bit rate associated with a video sequence of images. For each macro-block (MB) or sub-block within a MB for a target image to be encoded, various statistics are determined for a luminance component and chrominance components that provide at least average values for the chrominance components that may then be used to identify saturation thresholds. The saturation thresholds are used to determine a Qlevel. An average Qlevel from at least a previous frame is used to generate a Q modulation factor that may be combined with an activity based modulation factor and/or variance based modulation factor, or used singly. The final quantizer is calculated by bit-rate controller base quantizer multiplied by the Q modulation factor, and may be used to encode a MB or sub-block within a MB.