Color Management of Digital Media Art Images Based on Image Processing

Abstract

In recent years, there is a growing demand for high-quality color imaging of digital media art (DMA) images, along with the proliferation of smart mobile terminals and the Internet technology. However, the existing digital terminals cannot transmit or reproduce the color of DMA images satisfactorily. This paper explores the key techniques of color management of highly dynamic color DMA images, aiming to evaluate the exact quality and acquire abundant details of these images. Firstly, five indices were designed to evaluate the quality of highly dynamic color DMA images, namely, peak signal-to-noise ratio (PSNR), mean square error (MSE), Pearson linear correlation coefficient (PLCC), Kendall rank correlation coefficient (KRCC), and Spearman's rank correlation coefficient (SRCC). The workflow of image quality judgement was also introduced. To correct the DMA images under nonstandard illumination, a color correction method was proposed based on Retinex algorithm. In addition, a color reconstruction method was developed based on nonlocal Laplace energy function, solving the invalid and missing regions of single-frame color images. Finally, the effectiveness of our color management method was proved through experiments. The research results provide a reference for image quality valuation and color management in other fields.

Optimization of Digital Media Art Creation Process Based on Dual Transformer Residual Network (DTRN) and Lotus Effect Optimization Algorithm (LEA)

“…Artificial Intelligence (AI) aims to replicate human intelligence in computers, enabling machines to learn and solve problems like humans [1]. Visual communication encompasses the use of visual elements, such as animated GIFs, images, movies, diagrams, canvases, and slide deck presentations to communicate concepts or information [2]. Digital media education encompasses digital color art education as well, and knowledge of the state of digital media art research today offers insights examination of the state of digital color art education at the moment [3].…”

A New Deep Learning-Based Restoration Method for Colour Images

“…Images could be found in every corner of people's lives as they are an important carrier in this modern digital world. Especially the color images have been widely used in various fields including social media, medical diagnosis, distance education, commercial advertisement, and security monitoring due to their rich color information and excellent visual performance [1][2][3][4]. However, affected by factors such as camera equipment, transmission process and storage devices, colour images are often subject to noise pollution during acquisition, transmission and processing, which can adversely affect their visual quality and value in practical applications [5,6].…”

Application of Image Color Gamut Boundary Judgment Algorithm in Digital Media

“…With the development of various display media technologies, the application of color digital images in people's work and life is becoming more and more extensive, so the color difference evaluation of color digital images becomes increasingly important [1]. However, since a color digital image is composed of a large number of pixels of different colors, it is an uneven and complex color sample, which is difficult to measure directly with a colorimetric instrument [2], and it is also more complicated than a uniform color sample to calculate, so it has always been a color science and research difficulties in the field of imaging technology [3,4].…”