Bit-Depth Efficient Image Processing via Nonlinear Transformation
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Solution Overview
Problem
Conventional image processing systems struggle to generate wide dynamic range images efficiently due to limited bit depth, resulting in visible contours in images with uniform luminance, especially when displaying scenes with gradual brightness changes.
Innovation Solution
The system applies a nonlinear transformation to the captured image signals, optimizing bit depth allocation based on noise levels to minimize contour visibility, even when the output bit depth is lower than the image sensor's bit depth, and includes an inverse transformation to re-encode the image at a higher bit depth for improved dynamic range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If linear bit depth distribution is used across full brightness range, then hardware complexity is reduced, but contour artifacts appear in uniform brightness regions
Solution Approach 1:
The patent applies a nonlinear transformation (such as gamma correction or logarithmic transformation) to the captured image signals, which changes the parameter of bit depth distribution from linear to nonlinear. This transformation optimizes the allocation of bit depth resolution across different brightness ranges, assigning higher resolution to mid-tone regions where the human eye is most sensitive to brightness differences, thereby reducing contour artifacts without requiring increased hardware bit depth
Solution Approach 2:
The patent implements local quality optimization by applying different bit depth allocation strategies to different regions of the brightness spectrum. Specifically, it concentrates bit depth resolution in mid-tone regions where contour artifacts are most noticeable to human vision, while using coarser quantization in very dark and very bright regions where the human eye is less sensitive, thereby reducing overall contour visibility without uniformly increasing hardware complexity
2Manufacturing precision
If higher bit depth is used for wide dynamic range display, then image quality is improved, but power consumption increases
Solution Approach 1:
The patent transforms the image signal parameters using nonlinear functions (gamma correction, logarithmic transformation) to optimize the distribution of available bit depth. This allows the system to achieve perceptually superior wide dynamic range image quality using standard 8-bit or 10-bit displays, avoiding the need for high-bit-depth (12-bit or 16-bit) hardware that would consume significantly more power during processing and display
3Adaptability or versatility
If higher bit depth hardware is used, then dynamic range capability is improved, but system cost increases
Solution Approach 1:
The patent employs parameter transformation through nonlinear functions to enable standard displays with limited bit depth (8-bit or 10-bit) to effectively display wide dynamic range images. By optimizing the distribution of bit depth resolution and applying appropriate tone mapping, the system achieves high dynamic range capability without requiring expensive high-bit-depth display hardware, thereby reducing system cost while maintaining adaptability
Solution Approach 2:
The patent creates a transformed copy of the original high-dynamic-range image signal through nonlinear transformation. This transformed copy is optimized for display on standard displays with limited bit depth, allowing the system to reproduce wide dynamic range images on cost-effective hardware without directly requiring the original high-bit-depth hardware
Data Source
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AI summary
A computer-implemented method for bit-depth efficient image processing includes a step of communicating at least one non-linear transformation to an image signal processor. Each non-linear transformation is configured to, when applied by the image signal processor to a captured image having sensor signals encoded at a first bit depth, produce a nonlinear image that re-encodes the captured image at a second bit depth that may be less than the first bit depth, while optimizing allocation of bit depth resolution in the nonlinear image for low contour visibility. The method further includes receiving the nonlinear image from the image signal processor, and applying an inverse transformation to transform the nonlinear image to a re-linearized image at a third bit depth that is greater than the second bit depth. The inverse transformation is inverse to the nonlinear transformation used to produce the nonlinear image.