Noniterative Power-Constrained Contrast Enhancement Algorithm for OLED Display

Jang, Chan Young; Kang, Suk‐Ju; Kim, Young Hwan

doi:10.1109/jdt.2016.2595587

Cited by 10 publications

(12 citation statements)

References 13 publications

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“…Starting from the observation that chrominance variations in a photo or video strongly alter the perceived quality, they typically act only on the luminance of pixels. Some approaches try to concurrently reduce power consumption and enhance the target image through non-linear transformations [10], [11], [12], [13], [22]. A fundamental technique in this category is Power-Constrained Contrast Enhancement (PCCE), first described in [10].…”

Section: Background and Related Workmentioning

confidence: 99%

“…PCCE computes a nonlinear luminance mapping for each image, using an objective function that concurrently favors the reduction of power and the enhancement of contrast in the output. Many variants of PCCE have been proposed in literature; the solution in [11] uses multi-scale retinex to improve output quality, while [12] devises a similar transformation that does not require an iterative optimization procedure for extracting the luminance mapping. Transformations with similar objectives to PCCE, but adopting different algorithms are described in [13] and [22].…”

Section: Background and Related Workmentioning

confidence: 99%

“…The most popular solution is based on applying a transformation to the image shown on the panel, thus trading off the achievable saving and the "fidelity" of the content [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15]. Most transformations consist of mappings of pixel intensities, i.e tabular or analytical functions linking pixel intensity x in the input image to a corresponding value T (x) in the output image.…”

Section: Introductionmentioning

confidence: 99%

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Low-Overhead Adaptive Brightness Scaling for Energy Reduction in OLED Displays

Pagliari

Cataldo

Patti

et al. 2021

IEEE Trans. Emerg. Topics Comput.

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Organic Light Emitting Diode (OLED) is rapidly emerging as the mainstream mobile display technology. This is posing new challenges on the design of energy-saving solutions for OLED displays, specifically intended for interactive devices such as smartphones, smartwatches and tablets. To this date, the standard solution is brightness scaling. However, the amount of the scaling is typically set statically (either by the user, through a setting knob, or by the system in response to predefined events such as low-battery status) and independently of the displayed image. In this work we describe a smart computing technique called Low-Overhead Adaptive Brightness Scaling (LABS), that overcomes these limitations. In LABS, the optimal content-dependent brightness scaling factor is determined automatically for each displayed image, on a frame-by-frame basis, with a low computational cost that allows real-time usage. The basic form of LABS achieves more than 35% power reduction on average, when applied to different image datasets, while maintaining the Mean Structural Similarity Index (MSSIM) between the original and transformed images above 97%.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Low-Overhead Adaptive Brightness Scaling for Energy Reduction in OLED Displays

Pagliari

Cataldo

Patti

et al. 2021

IEEE Trans. Emerg. Topics Comput.

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“…As an OLED directly emits light at each pixel, the total power consumption of the display is known to be proportional to the sum of the power consumption of each pixel [3]- [5]. However, as the brightness of the overall pixels is reduced to save power, the visibility and contrast of the image with re- 1 duced brightness are also decreased.…”

Section: Introductionmentioning

confidence: 99%

“…To cope with this problem, Lee et al [3] proposed a power-constrained contrast enhancement (PCCE) technique that saves power while enhancing the contrast of the image to preserve visual quality. A variant of PCCE based on the multiscale retinex was introduced in [4], whereas Jang et al [5] proposed a non-iterative PCCE method for fast computation. Peng et al [6] combined a histogram shrinking technique with contrast enhancement to achieve the same goal as PCCE for real-time applications of full-HD displays.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Deep Contrast Enhancement With Power Constraint for OLED Displays

Shin

Park

Yeo

et al. 2020

IEEE Trans. on Image Process.

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Various power saving and contrast enhancement (PSCE) techniques have been applied to an organic light emitting diode (OLED) display for reducing the power demands of the display while preserving the image quality. In this paper, we propose a new deep learning-based PSCE scheme that can save power consumed by the OLED display while enhancing the contrast of the displayed image. In the proposed method, the power consumption is saved by simply reducing the brightness a certain ratio, whereas the perceived visual quality is preserved as much as possible by enhancing the contrast of the image using a convolutional neural network (CNN). Furthermore, our CNN can learn the PSCE technique without a reference image by unsupervised learning. Experimental results show that the proposed method is superior to conventional ones in terms of image quality assessment metrics such as a visual saliency-induced index (VSI) and a measure of enhancement (EME). 1 Index Terms-Convolutional neural network, deep learning, energy efficiency, image enhancement.

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