Power-constrained Contrast Enhancement Research Articles

Deep Battery Saver: End-to-End Learning for Power Constrained Contrast Enhancement

Due to the problems of power-hungry displays and limited battery life in electronic devices, the concept of “green computing,” which entails a reduction in power consumption, is proposed. One often seen green computing is the power-constrained contrast enhancement (PCCE), yet it is much more challenging because of the noticeable local intensity suppressions in images. This paper aims at developing an image-quality-lossless end-to-end learning network called deep battery saver to achieve power savings in emissive displays, i.e., produce power-saved images with high perceptual quality and less power consumption. Built upon the end-to-end network of the displayed image, we propose a variational loss function for enhancing the visual quality and suppressing the power consumption, simultaneously. The basic idea is to integrate both high-level perceptual losses and low-level pixel losses by a deep residual convolutional neural network (CNN) over a devised variational loss function with strong human perceptual consistency. Such deep residual CNN network leads to a visually pleasing image representation during the suppression of power consumption. Experimental results demonstrated the superiority of our deep battery saver to existing PCCE methods.

Unsupervised Deep Contrast Enhancement with Power Constraint for OLED Displays.

Various power-constrained contrast enhance-ment (PCCE) techniques have been applied to an organic light emitting diode (OLED) display for reducing the pow-er demands of the display while preserving the image qual-ity. In this paper, we propose a new deep learning-based PCCE scheme that constrains the power consumption of the OLED displays while enhancing the contrast of the displayed image. In the proposed method, the power con-sumption is constrained by simply reducing the brightness a certain ratio, whereas the perceived visual quality is pre-served as much as possible by enhancing the contrast of the image using a convolutional neural network (CNN). Furthermore, our CNN can learn the PCCE technique without a reference image by unsupervised learning. Ex-perimental results show that the proposed method is supe-rior to conventional ones in terms of image quality assess-ment metrics such as a visual saliency-induced index (VSI) and a measure of enhancement (EME).1.

Power-Constrained Image Contrast Enhancement Through Sparse Representation by Joint Mixed-Norm Regularization

Power-constrained image contrast enhancement is a fundamental step for improving the battery life of modern consumer devices with embedded emissive-display panels, such as organic light-emitting diodes (OLEDs). The conventional power-constrained image contrast enhancement in OLED displays is typically performed using histogram-relevant priors or heuristic curve-fitted techniques. This results in underexposure effects or color-tone changes in the reconstructed image. Therefore, this paper proposes a novel power-constrained sparse representation model by joint l2, 1, γ, and isotropic total variation norm (called mixed-norm) regularized sparse coding to simultaneously improve power saving and the perceptible visual quality of the OLED displays. The qualitative and quantitative experiments demonstrate that the proposed technique noticeably outperforms the state-of-the-art power-constrained contrastenhancement techniques.

Power constrained contrast enhancement based on brightness compensated contrast-tone mapping operation

We propose power constrained contrast enhancement based on brightness compensated contrast-tone mapping operation (BCCTMO). We adopt brightness compensation to restore the original perceptual luminance and combine tone mapping with contrast enhancement to improve image quality. First, we increase pixel values to compensate for the reduced brightness caused by backlight dimming while maintaining the perceived luminance. Then we perform a contrast-tone mapping operation to reduce the information loss caused by the brightness compensation and enhance contrast in images. Finally, we conduct color scaling to reproduce natural colors. We implement BCCTMO on an Android tablet with an LCD display using mathematical approximation. Experimental results demonstrate that BCCTMO produces high-quality images while minimizing distortions and saving power compared with state-of-the-art methods.

Noniterative Power-Constrained Contrast Enhancement Algorithm for OLED Display

This paper proposes a new power-constrained contrast enhancement algorithm for organic light-emitting diode displays that provides high perceptual image quality with low power consumption. Unlike existing methods, the proposed method can achieve the target power consumption (TPC) without iteration, while significantly alleviating overenhancement artifacts using global and local attributes compared to the benchmark methods. The proposed method consists of three steps. In the first step, mapping functions, which map the input intensity into the output intensity, are generated considering the TPC. In the second step, to properly alleviate the overenhancement artifacts, the proposed method adjusts the degree of contrast improvement in the mapping functions based on a global attribute that represents the contrast of the input image. Furthermore, the proposed method modifies the mapping functions using a local attribute, the spatial correlation between the input and output images, to alleviate the spatial distortion. Finally, using the modified mapping functions, the output image is generated to accurately meet the TPC. In the experiments, the proposed method increased the structural fidelity, enhancement performance measure, and sharpness by up to 13.1%, 65.7%, and 44.7% compared to the benchmark methods. Furthermore, it reduced the execution time by up to 28.1% and 91.6% compared to the benchmark methods and noniteratively met the TPC within an error rate of approximately 0.43%.

Power‐constrained contrast enhancement for organic light‐emitting diode display using locality‐preserving histogram equalisation

Display capabilities of the smart mobile devices are highly developed for the last decade. Thus, it is necessary to consider both quality of the images and the power consumed by the display. In this study, a power-constrained contrast enhancement method formulated as an optimisation problem (quadratically constrained quadratic programming) is proposed. The proposed locality condition is used to enhance the conventional histogram equalisation, where power constraint is used to specify the power-consumption ratio of the enhanced image. The experimental results show that the proposed method effectively enhances the quality of single images with designated power reduction achieved. The proposed algorithm is also applied to video sequences with a simple and effective update algorithm in real-time. The update algorithm gives successful results without flickering artefacts observed in existing methods.

Survey on Implementation and Analysis of Power-Constrained Contrast Enhancement Algorithm Using a Sub-band Decomposed MSR for OLED Display

Survey on Implementation and Analysis of Power-Constrained Contrast Enhancement Algorithm Using a Sub-band Decomposed MSR for OLED Display

Power-constrained contrast enhancement algorithm using multiscale retinex for OLED display.

This paper presents a power-constrained contrast enhancement algorithm for organic light-emitting diode display based on multiscale retinex (MSR). In general, MSR, which is the key component of the proposed algorithm, consists of power controllable log operation and subbandwise gain control. First, we decompose an input image to MSRs of different sub-bands, and compute a proper gain for each MSR. Second, we apply a coarse-to-fine power control mechanism, which recomputes the MSRs and gains. This step iterates until the target power saving is accurately accomplished. With video sequences, the contrast levels of adjacent images are determined consistently using temporal coherence in order to avoid flickering artifacts. Finally, we present several optimization skills for real-time processing. Experimental results show that the proposed algorithm provides better visual quality than previous methods, and a consistent power-saving ratio without flickering artifacts, even for video sequences.

Power-Constrained Contrast Enhancement for Emissive Displays Based on Histogram Equalization

A power-constrained contrast-enhancement algorithm for emissive displays based on histogram equalization (HE) is proposed in this paper. We first propose a log-based histogram modification scheme to reduce overstretching artifacts of the conventional HE technique. Then, we develop a power-consumption model for emissive displays and formulate an objective function that consists of the histogram-equalizing term and the power term. By minimizing the objective function based on the convex optimization theory, the proposed algorithm achieves contrast enhancement and power saving simultaneously. Moreover, we extend the proposed algorithm to enhance video sequences, as well as still images. Simulation results demonstrate that the proposed algorithm can reduce power consumption significantly while improving image contrast and perceptual quality.