Image Processing for Machine Vision Applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

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

MTFCalculator - A mobile application for measuring the Modulation Transfer Function of built-in cameras of smartphones using ISO 12233 slanted-edge method

The Modulation Transfer Function (MTF) is the most commonly used measure to test the performance of optical systems. A reliable and least complex method to find the MTF is by using the slanted-edge target, an ISO 12233 standard for measuring the quality of image capturing devices. The computation of MTF is a multi-step process, in which the three main components are the Edge Spread Function (ESF), the Line Spread Function (LSF), and the MTF. This paper presents the development of a mobile application to measure the MTF50 values to assess the performance of built-in smartphone cameras. Experiments have been performed using six different smartphones, including, Huawei Mate 10 Pro, Huawei P20 Pro, iPhone XS Max, iPhone 11, iPhone 11 Pro, and JiaYu S3. The experimental results are compared with Imatest, which is one of the most leading tools for testing the performances of imaging devices. The results of both the applications are exactly the same in terms of ranking the smartphones according to their MTF50 values

On producing energy-efficient and contrast-enhanced images for OLED-based mobile devices

Organic Light-Emitting Diode (OLED) display panels have become increasingly popular in recent years due to their numerous advantages over traditional LCDs. The power consumption in OLED-based displays highly depends on the displayed contents. This paper utilizes the image-dependent power-consuming quality of OLED displays to produce energy-efficient and contrast-enhanced images that consume less power and have better visual quality compared to their original form when displayed on an OLED device. The goal of reducing the power consumption in images is achieved by lowering the RGB intensity levels in color images. This idea is inspired by the “Land-Effect”, which reveals that a full-color image can be generated by using only two color components instead of three. However, in this work, a modified Land-Effect has been proposed to yield better results as compared to the original Land-Effect. The proposed method also uses some image enhancement algorithms such as white balance and retinex filter to produce quality enhanced images that have visually better color contrasts as compared to the original images. The real power consumption of the modified images was measured on Samsung Galaxy A50, an OLED-based mobile device. The experimental results demonstrated that the proposed method reduced the power consumption by 13.16% on average. Moreover, to assess the visual quality of images and to understand the user acceptability of the modified images, a subjective evaluation was also performed. The subjective test results showed a higher preference rate for the modified images as compared to the original images. The proposed system in its current form is suitable for server-side implementation where an image would be modified once and the modified image could be used indefinitely by millions of users on social media platforms