Rate-Control-Aware Reshaping for HDR Image Coding Efficiency
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Solution Overview
Problem
Traditional reshaping techniques for high-dynamic range (HDR) images are independent of rate-control mechanisms, leading to inefficient bit allocation and increased coding artifacts, particularly in scenes with complex textures or noisy content.
Innovation Solution
The development of rate-control-aware reshaping functions that adjust the codeword range and reshaping mappings based on noise metrics and scene characteristics, allowing for intelligent bit allocation and improved coding efficiency by reducing the codeword range for noisy HDR content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional reshaping techniques are used for HDR images, then the coding process is simple and independent of rate-control mechanisms, but bit allocation becomes inefficient and coding artifacts increase in noisy content
Solution Approach 1:
The reshaping function is made dynamic by adjusting the codeword range based on scene characteristics and noise metrics. The system adapts the reshaping parameters in real-time according to the content being encoded, rather than using a fixed reshaping function. This allows the coding process to optimize bit allocation for different types of content, improving overall coding efficiency while maintaining manageable complexity through automated adaptation.
Solution Approach 2:
The invention changes the parameters of the reshaping function based on noise metrics and scene characteristics. By computing noise metrics for different regions and adjusting the codeword range accordingly, the system optimizes bit allocation for noisy versus clean content. This parameter adaptation resolves the contradiction by making the reshaping process content-aware, improving coding efficiency without requiring complex manual intervention.
2Productivity
If the codeword range is reduced for noisy HDR content, then bit usage decreases and coding efficiency improves, but visual quality may be compromised
Solution Approach 1:
The invention applies different reshaping strategies to different regions of the image based on local noise characteristics. By computing noise metrics for specific regions and applying localized codeword range adjustments, the system preserves visual quality in clean regions while reducing bit usage in noisy regions. This local adaptation resolves the contradiction by ensuring that visual quality is maintained where needed while optimizing coding efficiency where possible.
Solution Approach 2:
The system uses noise metrics as feedback to dynamically adjust the reshaping function and codeword range. By continuously evaluating the noise characteristics of the content and adapting the coding parameters accordingly, the system optimizes the balance between coding efficiency and visual quality. This feedback mechanism ensures that bit allocation decisions are based on actual content characteristics rather than fixed rules.
3Productivity
If rate-control-aware reshaping is implemented, then bit allocation becomes efficient and coding artifacts are reduced, but the complexity of the reshaping process increases
Solution Approach 1:
The reshaping system performs self-service by automatically computing noise metrics and adjusting its own parameters based on the content being encoded. The system independently evaluates the noise characteristics and adapts the codeword range without requiring external intervention or complex control mechanisms. This self-adaptation reduces the operational complexity despite the increased algorithmic complexity, as the system manages its own optimization autonomously.
Data Source
AI summary
Given an input image in a high dynamic range (HDR) which is mapped to a second image in a second dynamic range using a reshaping function, to improve coding efficiency, a reshaping function generator may adjust the codeword range of the HDR input under certain criteria, such as for noisy HDR images with a relatively-small codeword range. An example of generating a scaler for adjusting the HDR codeword range based on the original codeword range and a metric of the percentage of edge-points in the HDR image is provided. The adjusted reshaping function allows for more efficient rate control during the compression of reshaped images.