Gamma, Brightness, and Luminance Considerations for HD Displays

Kykta, Martin

doi:10.1002/j.2637-496x.2009.tb00117.x

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…This creates a bright and clear effect for the color image. Gamma correction compensates for the color display differences that exist on different output devices, making the image appear the same on different monitors [40]. It also enhances the dynamic range and detail in the dark areas of the image to better respond to the human eye's sensitivity to dark areas [41].…”

Section: Traditional Isp Principle and Pipelinementioning

confidence: 99%

HISP: Heterogeneous Image Signal Processor Pipeline Combining Traditional and Deep Learning Algorithms Implemented on FPGA

Chen,

Wang,

et al. 2023

Electronics

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To tackle the challenges of edge image processing scenarios, we have developed a novel heterogeneous image signal processor (HISP) pipeline combining the advantages of traditional image signal processors and deep learning ISP (DLISP). Through a multi-dimensional image quality assessment (IQA) system integrating deep learning and traditional methods like RankIQA, BRISQUE, and SSIM, various partitioning schemes were compared to explore the highest-quality imaging heterogeneous processing scheme. The UNet-specific deep-learning processing unit (DPU) based on a field programmable gate array (FPGA) provided a 14.67× acceleration ratio for the total network and for deconvolution and max pool, the calculation latency was as low as 2.46 ms and 97.10 ms, achieving an impressive speedup ratio of 46.30× and 36.49× with only 4.04 W power consumption. The HISP consisting of a DPU and the FPGA-implemented traditional image signal processor (ISP) submodules, which scored highly in the image quality assessment system, with a single processing time of 524.93 ms and power consumption of only 8.56 W, provided a low-cost and fully replicable solution for edge image processing in extremely low illumination and high noise environments.

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Section: Traditional Isp Principle and Pipelinementioning

confidence: 99%

HISP: Heterogeneous Image Signal Processor Pipeline Combining Traditional and Deep Learning Algorithms Implemented on FPGA

Chen,

Wang,

et al. 2023

Electronics

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“…Therefore, we have written software, which converges ΔE toward ‘1’ for darker gray tones under bright incident light. Our other approach ‘ΔL/L’ (Equation ) corresponds to the Weber fraction (also known as Weber‐Fechner or Weber's law,), with the reference luminance L as the mean value of the luminance of the neighboring gray tones (L n , L n+1 ). The intention of this gray scale enhancement strategy is to match the Weber fraction and/or DICOM characteristics as closely as possible; both also depend on the luminance of the display.…”

Section: Image Enhancementmentioning

confidence: 99%

Optimizing and evaluating new automotive HMI image enhancement algorithms under bright light conditions using display reflectance characteristics

et al. 2014

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-We have developed and investigated new image enhancement (tone mapping) algorithms for automotive Human Machine Interfaces (HMIs) so that displays can be better read in bright light. Our 'ΔL/L' approach with adaptive color rendering (CR) is based on Weber's fraction and is easy to use with various displays and illumination variables. In tests with automotive HMIs under different lighting conditions -including driver glare-the 'ΔL/L + CR' image enhancement method proved to be a good choice. First, reflective, transflective, and transmissive displays were tested and their image quality compared with and without new tone mapping. It was found that enhancing improved the gray scale perception, even on reflective (e-paper by dithering) displays. A transmissive LCD, which only reflects monochrome, is highly suitable for mostly monochrome content as it maintains a high contrast ratio under bright light; however, its gamut is degraded at large extent. Transmissive matte LCDs perform best with image enhancement for automotive color HMIs.

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“…In 2006, Devlin et al [26] proposed a luminance remapping based on human eye just-noticeable difference (JND) measurements to compensate for the loss of contrast, with visual calibration performed by the viewers themselves. Kykta et al [27] applied two gamma curves to fit the human eye function, though they did not consider ambient light. In 2014, Blankenbach et al [28] corrected the display outputs based on the Weber fraction with color rendering.…”

Section: Introductionmentioning

confidence: 99%

Enhancing a Display’s Sunlight Readability with Tone Mapping

Qian,

Chen,

Hsiang

et al. 2024

Photonics

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The sunlight readability of display devices, such as notebook computers, transparent displays, vehicle displays, and augmented reality, is a significant technical challenge due to degraded image quality. To mitigate this problem, by fitting the human eye function, we propose a tone mapping method on a mobile phone display panel to enhance low grayscale image readability under bright ambient light. Additionally, we adapt a mini-LED backlight model to simulate real images under different ambient lighting conditions. Both experimental and simulated results indicate that high luminance displays with an optimized gamma value significantly enhance sunlight readability and image quality. Moreover, global color rendering can alleviate color shift. Such a method is also valid for the optimization of optical see-through devices under diverse environmental conditions.

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Gamma, Brightness, and Luminance Considerations for HD Displays

Cited by 3 publications

References 5 publications

HISP: Heterogeneous Image Signal Processor Pipeline Combining Traditional and Deep Learning Algorithms Implemented on FPGA

HISP: Heterogeneous Image Signal Processor Pipeline Combining Traditional and Deep Learning Algorithms Implemented on FPGA

Optimizing and evaluating new automotive HMI image enhancement algorithms under bright light conditions using display reflectance characteristics

Enhancing a Display’s Sunlight Readability with Tone Mapping

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