Producing green computing images to optimize power consumption in OLED-based displays

Energy consumption in Organic Light Emitting Diode (OLED) depends on the displayed contents. The power consumed by an OLED-based display is directly proportional to the luminance of the image pixels. In this paper, a novel idea is proposed to generate energy-efficient images, which consume less power when shown on an OLED-based display. The Blue color component of an image pixel is the most power-hungry i.e. it consumes more power as compared to the Red and Green color components. The main idea is to reduce the intensity of the blue color to the best possible level so that the overall power consumption is reduced while maintaining the perceptual quality of an image. The idea is inspired by the famous “Land Effect”, which demonstrates that it is possible to generate a full-color image by using only two color components instead of three. experiments are performed on the Kodak image database. The results show that the proposed method is able to reduce the power consumption by 18% on average and the modified images do not lose the perceptual quality. Social media platform, where users scroll over many images, is an ideal application for the proposed method since it will greatly reduce the power consumption in mobile phones during surfing social networking applications

Image Processing for Machine Vision Applications

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A Linear-Time Algorithm of Low-Power Histogram Equalization for OLED Displays

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 김태환.영상의 대비를 향상 시키기 위해 널리 사용되는 방법 중 하나가 히스토그램 평준화(Histogram equalization, HE)이다. 그러나 소비전력을 고려하지 않은 히스토그램 평준화를 유기 발광 다이오드(organic light-emitting diode, OLED) 디스플레이(display)에 적용하게 되면 디스플레이의 소비전력을 증가 시키는 한계가 있다. 이 연구에서는 선형 시간에 계산이 가능한 저전력 히스토그램 평준화 알고리즘을 제안하였다. 이 알고리즘은 히스토그램 평준화를 적용한 영상의 후 처리 과정으로 사용되어 히스토그램 평준화의 한계를 극복할 수 있다. 더 나아가 저전력 알고리즘을 통해 OLED 디스플레이의 소비전력을 감소 시키고 미미하게 감소된 평균 밝기는 히스토그램 평준화를 이용하여 영상의 대비를 향상 시켜 보상하는 방법으로 OLED 디스플레이의 소비전력 문제를 해결하는 하나의 방법으로도 사용될 수 있다. 우리가 제안한 알고리즘은 히스토그램 평준화에 의해 변형된 영상의 밝기 단계(gray level)를 조절하여 OLED 디스플레이의 전력 소모를 줄였다. 이 알고리즘은 히스토그램 평준화를 통해 변형된 영상의 밝기 단계 중 많은 수가 실제로는 사용되지 않는다는 분석에 기초를 두고 있다. 영상에서 사용되지 않은 밝기 단계를 제거할 때 영상의 대비(contrast) 변화를 최소화하면서 영상을 변형 시키는 것이 이 알고리즘의 핵심이다. 24개의 코닥(Kodak) 영상을 사용하여 실험한 결과 우리가 제안한 알고리즘은 전력을 고려하지 않은 히스토그램 평준화 알고리즘 대비 소비전력을 64%까지 감소 시킬 수 있었다. 또한 기존에 제안 된 소비전력을 고려한 히스토그램 평준화 알고리즘과는 영상의 대비, 감소된 소비전력 측면에서 비슷한 수준의 효과를 보였다. 그러나 알고리즘의 수행 속도를 기존의 저 소비전력 알고리즘 대비 443배 향상 시킬 수 있었다.Histogram equalization (HE) is a widely used technique to enhance the contrast of images. However, one critical limitation of the HE based image enhancement techniques is the unawareness of power consumption. In this work, we propose a linear-time power-aware image reformation algorithm, which can be used as a post-processing of HE based image transformations, to overcome the limitation. Precisely, our proposed algorithm refines the images produced by HE based image transformations to reduce power consumption in organic light-emitting diode (OLED) display while preserving the quality of image.The key idea is based on the observation that a large number of gray levels are never used in the HE images, which implies that those gray levels can be efficiently and effectively exploited to reduce power consumption with an easiness of maintaining the image contrast. Through experiments with 24 Kodak images, it is shown that our proposed algorithm incorporating the idea is able to reduce the power consumption by 64% over that of the power unaware conventional HE transformation algorithm,and speed up the processing time by 443 times faster over that of the power aware conventional HE transformation algorithm while achieving almost the same contrast/brightness and power saving.요약(국문초록) 제 1 장 서론 제 2 장 본론 2.1 개요 및 배경 이론 2.2 관찰 및 분석 2.3 알고리즘 2.4 실험 제 3 장 결론 참고문헌 AbstractMaste

LAPSE: Low-Overhead Adaptive Power Saving and Contrast Enhancement for OLEDs

Organic Light Emitting Diode (OLED) display panels are becoming increasingly popular especially in mobile devices; one of the key characteristics of these panels is that their power consumption strongly depends on the displayed image. In this paper we propose LAPSE, a new methodology to concurrently reduce the energy consumed by an OLED display and enhance the contrast of the displayed image, that relies on image-specific pixel-by-pixel transformations. Unlike previous approaches, LAPSE focuses specifically on reducing the overheads required to implement the transformation at runtime. To this end, we propose a transformation that can be executed in real time, either in software, with low time overhead, or in a hardware accelerator with a small area and low energy budget. Despite the significant reduction in complexity, we obtain comparable results to those achieved with more complex approaches in terms of power saving and image quality. Moreover, our method allows to easily explore the full quality-versus-power tradeoff by acting on a few basic parameters; thus, it enables the runtime selection among multiple display quality settings, according to the status of the system

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

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%

화질 개선을 위한 히스토그램 평활화 및 소셜 네트워크에서의 링크 예측 알고리즘

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 김태환.Data processing method is exploited to obtain the expected results by processing and analyzing the input data. In this research, we have focused on processing image and social network data in the area of image processing and social network analysis respectively by using data processing method. Gray-level context-driven histogram equalization and community-adaptive link prediction are proposed for image processing and social network analysis respectively. The abstractions of these two data processing methods are as follows. First, histogram equalization, which redefines the distribution of gray-levels in an image, is an important step in image processing to enhance the image quality. Until now, numerous histogram equalization techniques have been proposed, among which the majority of them have focused on solving the problem of how the gray-levels in the histogram of an input image should be properly partitioned so that the image produced by collecting all equalization results for the partitioned sub-histograms leads to the quality enhancement of the image. However, the partition based equalization methods have an inherent limitation of not being able to equalize a sub-histogram crossing a partition boundary, which is the main cause of image distortion. In this work, we propose a gray-level context-driven histogram equalization method to overcome this limitation. In short, rather than constraining disjoint mapping ranges of the gray-levels among the partitions, we devise two enabling techniques: (1) a mapping range for each gray-level with no range-disjoint constraint and (2) a mapping distance between two adjacent gray-levels to make a full exploitation of mapping flexibility of gray-levels. We formulate the histogram equalization problem integrating the two techniques into a flow optimization problem in a specially designed structure of a network, and solve it globally and efficiently. In addition, we seamlessly combine the factor of power consumption into our network flow optimization formulation to make an easy trade-off between image quality and power saving. Second, Link prediction is one of hot research topics in social network analysis. Link prediction problem is to find a small set of node pairs in the networks that are not directly connected, but will be very likely to be connected in the future. To improve the prediction accuracy, many works have attempted to consider the community information, if available, in the social network structure. One common strategy of the prior community-aware link prediction algorithms is that they devised a sort of unified link prediction formulation that simply includes a premium term to express whether a link is structurally in the same community or not. However, since the formulation of the premium term relies on the structural formation of communities only, it cannot take into account the fact that the communities in different social networks, though they form almost identical community structures, can make different levels of influence on the link prediction. To cope with this limitation, we propose an adaptive approach, in which we use two separate link predictions depending on inter or intra-links in community, and then balance the links based on the degree of community influence on link prediction. In conclusion, through experiments with the diverse datasets, it is shown that our proposed gray-level context-driven histogram equalization and community-adaptive link prediction are able to achieve much more improved performance compared to previous data processing methods in each of the image processing and social network analysis area.1 Introduction 1 1.1 Introduction 1 1.2 Histogram Equalization in Image Enhancement 2 1.3 Link Prediction in Social Networks 2 2 Gray-Level Context-Driven Histogram Equlaization 5 2.1 Introduction 5 2.2 Enabling Techniques for Equalization 9 2.2.1 Fine-Grained Gray-Level Mapping Range 9 2.2.2 Context-Driven Mapping Distance 13 2.3 The Network Flow Formulation 22 2.4 Integration of Power Minimization 25 2.5 Experimental Results 26 2.6 Summary 44 3 Community-Adaptive Link Prediction in Social Networks 48 3.1 Introduction 48 3.2 Related Works 50 3.3 Algorithm for Community-Adaptive Link Prediction 52 3.4 Experimental Results 55 3.5 Summary 63 4 Conclusion 64 4.1 Gray-Level Context-Driven Histogram Equalization 64 4.2 Community-Adaptive Link Prediction in Social Networks 65 Abstract in Korean 73Docto