Dynamic Range Scaling for HDR Color Component Quantization
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Solution Overview
Problem
Image processing methods suffer from precision loss during quantization, particularly when converting high dynamic range (HDR) images from floating-point to fixed-point formats, leading to reduced image quality.
Innovation Solution
An image processing method and apparatus that determine the dynamic range of color components, calculate a scaling factor based on this range and a permissible range, and apply scaling to improve image quality by expanding the dynamic range, thereby reducing precision loss during quantization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pixel values are converted from floating-point to fixed-point through quantization, then data can be processed by conventional video codecs, but precision loss occurs leading to reduced image quality
Solution Approach 1:
The patent applies scaling to expand the dynamic range of color component values before quantization converts them from floating-point to fixed-point format. This preliminary expansion ensures that the quantization process operates on an optimized range, minimizing precision loss while maintaining compatibility with conventional video codecs that process fixed-point data.
Solution Approach 2:
The patent changes the parameters of color component values by applying scaling factors to expand their dynamic range. This parameter transformation allows the values to better utilize the fixed-point representation space, reducing quantization error and preserving image quality while enabling processing by standard video codecs.
2Productivity
If HDR images are converted to fixed-point format for encoding, then they can be compressed and transmitted, but precision loss occurs during the conversion process
Solution Approach 1:
The patent performs scaling to expand the dynamic range of color component values before the quantization step. This preliminary action ensures that when HDR images are converted to fixed-point format for efficient encoding and transmission, the values are optimally positioned to minimize precision loss, thereby preserving image quality while achieving productive compression.
Solution Approach 2:
The patent transforms the parameters of color component values through scaling operations, expanding their dynamic range to better match the fixed-point format requirements. This parameter change reduces information loss during quantization while maintaining encoding efficiency for compression and transmission.
3Ease of operation
If color component values are directly quantized without scaling, then the process is simple and fast, but precision loss is significant reducing image quality
Solution Approach 1:
The patent introduces a scaling step that expands the dynamic range of color component values before quantization. While this adds a preliminary operation, it significantly improves the precision of color component representation during quantization, reducing precision loss and enhancing overall image quality while maintaining operational simplicity.
Solution Approach 2:
The patent applies parameter changes through scaling factors to expand the dynamic range of color component values. This transformation optimizes the values for subsequent quantization, improving manufacturing precision of color representation while keeping the overall process straightforward and easy to implement.
Data Source
AI summary
The present disclosure proposes an image processing method according to an embodiment. The image processing method includes the operations of obtaining a color component value from an input image, determining a dynamic range of the obtained color component value, determining a scaling factor for converting the dynamic range of the color component value, based on the determined dynamic range of the color component value and a permissible range of the color component value, and scaling the color component value, based on the determined scaling factor.