Image Dimming Perceptual Model Based Pixel Compensation and Backlight Adjustment

Chen, Jiazhong; Ma, Bingpeng; Li, Rong; Xia, Tao; Cao, Han

doi:10.1109/jdt.2015.2436403

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…Conventionally, the backlight distribution is simulated by convolving the BL signals with the light spread function (LSF), which represents the spatial intensity distribution of each LED segment. According to the convolution result, the compensated image signals are derived, and an HDR image can be displayed by combining the BL signal and the compensated image signals . Because the LSF method involves the convolution operation, its computational complexity and hardware loading are very high .…”

Section: Pixel Compensation Methodsmentioning

confidence: 99%

High‐performance local‐dimming algorithm based on image characteristic and logarithmic function

Zhang

Wang

et al. 2018

J Soc Info Display

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With the emergence of high-dynamic range (HDR) image and video, displaying an HDR image or video on a standard dynamic range displays with natural visual effect, clear details on local areas, and high-contrast ratio has become an important issue. To achieve HDR, local-dimming technique has been commonly practiced. In this paper, we investigate the problem of local dimming for LEDbacklight LC display to provide algorithm support for developing HDR displays.A novel local-dimming algorithm is proposed to improve the contrast ratio, enhance the visual quality, and reduce the power consumption of the LCDs.The proposed algorithm mainly consists of two parts. The first part is a backlight luminance extraction method based on dynamic threshold and the maximum grayscale of an image block to improve the contrast ratio and reduce the power consumption. In the second one, a pixel compensation method based on logarithmic function is used to improve the visual quality and contrast ratio. At the same time, in order to better smooth the backlight diffusion at the edges of the backlight luminance signal to enhance the accuracy of the pixel compensation, we draw on the idea of BMA and improve it to establish the backlight diffusion model with different low-pass-filter templates for different types of blocks. Simulation and measured results show that the proposed algorithm outperforms the competing ones in contrast ratio, visual quality, and power-saving ratio.

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Section: Pixel Compensation Methodsmentioning

confidence: 99%

High‐performance local‐dimming algorithm based on image characteristic and logarithmic function

Zhang

Wang

et al. 2018

J Soc Info Display

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show abstract

“…Therefore, we need to smooth the backlight brightness of each block. In this paper, we use blur masking algorithm to smooth the backlight brightness . And the pixel compensation is calculated by using

L_{com} = L_{ori} \times {(\frac{255}{{italicBL}_{italicsmooth}})}^{1 / normalγ},

where L ori denotes the original pixel value and L com denotes the pixel value after the pixel compensation.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…In this paper, we use blur masking algorithm to smooth the backlight brightness. 22,23 And the pixel compensation is calculated by using…”

Section: Pixel Compensationmentioning

confidence: 99%

A rapid local backlight dimming method for interlaced scanning video

et al. 2018

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This paper presents a rapid local backlight dimming method for interlaced video to improve the efficiency of displaying interlaced video. In the proposed method, we combine the line average deinterlacing algorithm with the local backlight dimming algorithm. In order to reduce the computational complexity and the backlight power consumption, three novel methods are proposed. The first is to add the sparse method to process the deinterlaced image. Second, the improved dynamic threshold method is used to calculate the dimming factor. And third, the odd field and even field of the same frame use the same backlight brightness to do backlight dimming. The simulation results show that the proposed method can reduce the computational complexity and power consumption, and the experiment results verify that the method can provide high display effect.

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“…Pixel compensation is a widely used technique to compensate for visual distortion because its calculation is simple, and it is highly effective in image recovery. For example, Kang et al [2] and Chen et al [3] attempted to combine pixel compensation with the structural similarity (SSIM) index [4], which is an image quality assessment (IQA) metric that reflects the human visual system (HVS). They calculated a target error based on the SSIM index and limited backlight dimming so that the target error in the image quality following pixel compensation is not exceeded.…”

Section: Introductionmentioning

confidence: 99%

“…This method can lead to more HVS-aware results than other techniques using HE or other contrast enhancement techniques [11][12][13][14]. The proposed method conducts restricted pixel compensation to prevent image distortion, which is exposed by existing pixel compensation techniques [2][3][4][5][6][7][8]. If further restoration of the image brightness is required, additional image improvement is carried out by HS, and optimal visual satisfaction results are obtained based on the SSIM [4] index by using the parameters in the proposed HS.…”

Section: Introductionmentioning

confidence: 99%

Human Visual System-aware Dimming Method Combining Pixel Compensation and Histogram Specification

2017

KSII TIIS

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In thin-film transistor liquid-crystal displays (TFT-LCDs), which are most commonly used in mobile devices, the backlight accounts for about 70% of the power consumption. Therefore, most low-power-related studies focus on realizing power savings through backlight dimming. Image compensation is performed to mitigate the visual distortion caused by the backlight dimming. Therefore, popular techniques include pixel compensation for brightness recovery and contrast enhancement, such as histogram equalization. However, existing pixel compensation techniques often have limitations with respect to blur owing to the pixel saturation phenomenon, or because contrast enhancement cannot adequately satisfy the human visual system (HVS). To overcome these, in this study, we propose a novel dimming technique to achieve both power saving and HVS-awareness by combining the pixel compensation and histogram specifications, which convert the original cumulative density function (CDF) by designing and using the desired CDF of an image. Because the process of obtaining the desired CDF is customized to consider image characteristics, histogram specification is found to achieve better HVS-awareness than histogram equalization. For the experiments, we employ the LIVE image database, and we use the structural similarity (SSIM) index to measure the degree of visual satisfaction. The experimental results show that the proposed technique achieves up to 15.9% increase in the SSIM index compared with existing dimming techniques that use pixel compensation and histogram equalization in the case of the same low-power ratio. Further, the results indicate that it achieves improved HVS-awareness and increased power saving concurrently compared with previous techniques.

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Image Dimming Perceptual Model Based Pixel Compensation and Backlight Adjustment

Cited by 6 publications

References 13 publications

High‐performance local‐dimming algorithm based on image characteristic and logarithmic function

High‐performance local‐dimming algorithm based on image characteristic and logarithmic function

A rapid local backlight dimming method for interlaced scanning video

Human Visual System-aware Dimming Method Combining Pixel Compensation and Histogram Specification

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