Pixel Quantization Rounding Mode Selection for Temporal Coherence
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Solution Overview
Problem
Existing methods for quantizing floating values of pixels in images often result in temporal quantization incoherencies, particularly when converting dynamic ranges, leading to inefficiencies in coding high dynamic range images and increased residual prediction errors.
Innovation Solution
A method that involves obtaining a test image by reducing the dynamic range of a second image, quantizing its floating point values, and then using these quantized values to determine the rounding of floating point values in the original image, selecting between rounding to the greater, lesser, or nearest whole number based on differences and thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional rounding methods are used to quantize floating values of pixels, then the quantization process is simple and fast, but temporal quantization incoherencies appear between successive images
Solution Approach 1:
The patent applies preliminary action by pre-determining the rounding mode for each pixel position based on a test image before actually quantizing the target image. The rounding mode selection is performed in advance by analyzing a test image with the same dynamic range reduction, and then this pre-selected rounding mode is applied to the target image, ensuring temporal coherence without sacrificing quantization speed.
Solution Approach 2:
The patent uses copying by creating a test image that replicates the processing conditions (dynamic range reduction) applied to the target image. This test image serves as a model to determine the appropriate rounding modes, allowing the system to learn from the test case and apply the same rounding behavior to the target image, thereby ensuring temporal consistency across successive images.
2Adaptability or versatility
If dynamic range reduction is applied to convert high dynamic range images to standard dynamic range, then the images can be displayed on standard screens, but temporal quantization incoherencies and increased residual prediction errors occur
Solution Approach 1:
The patent applies local quality by selecting different rounding modes (rounding to greater whole number, lesser whole number, or nearest whole number) for different pixel positions based on local characteristics observed in the test image. This localized rounding mode selection optimizes the quantization quality for each region while maintaining temporal coherence, thereby reducing residual prediction errors and preserving coding efficiency.
3Measurement precision
If floating values are used to represent pixel components with greater than 8 bits dynamic range, then the dynamic range representation capability is improved, but quantization complexity increases
Solution Approach 1:
The patent reduces quantization complexity by performing the complex rounding mode selection in advance on a test image. The preliminary analysis determines the appropriate rounding mode for each pixel position, and this pre-computed information is then reused for the actual target image quantization, avoiding repeated complex decisions and simplifying the real-time processing.
Data Source
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AI summary
The invention relates to a method and device for quantising the floating value of a pixel of an image by rounding either to a lesser whole number, to a greater whole number, or to the whole number closest to this floating value. The method is characterised in that the selection of rounding this floating value is determined based on a test value.