Image Quality Assessment of Computer-generated Images: Based on Machine Learning

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文件名称：Image Quality Assessment of Computer-generated Images: Based on Machine Learning
文件大小：2.98MB
文件格式：PDF
更新时间：2021-08-19 17:59:45
机器学习 The measure of image (videos) quality remains a research challenge and a very active field of investigation considering image processing. One solution consists of providing a subjective score to the image quality (according to a reference or without reference) obtained from human observers. The setting of such psycho-visual tests is very expensive (considering time and human organization) and needs clear and strict proceedings. Algorithmic solutions have been developed (objective scores) to avoid such tests. Some of these techniques are based on the modeling of the Human Visual System (HVS) to mimic the human behavior, but they are complex. In the case of natural scenes, a great number of image (or video) quality databases exist that makes possible the validation of these different techniques. Soft computing (machine learning, fuzzy logic, etc.), widely used in many scientific fields such as biology, medicine, management sciences, financial sciences, plant control, etc., is also a very useful cross-disciplinary tool in image processing. These tools have been used to establish image quality and they are now well known. Emerging topics these last years concern image synthesis, applied in virtual reality, augmented reality, movie production, interactive video games, etc. For example, unbiased global illumination methods based on stochastic techniques can provide photo-realistic images in which content is indistinguishable from real photography. But there is a price: these images are prone to noise that can only be reduced by increasing the number of computed samples of the involved methods and consequently increasing their computation time. The problem of finding the number of samples that are required in order to ensure that most of the observers cannot perceive any noise is still open since the ideal image is unknown. Image Quality Assessment (IQA) is well known considering natural scene images. Image quality (or noise evaluation) of computer-generated images is slightly different, since image generation is different and databases are not yet developed. In this short book, we address this problem by focusing on visual perception of noise. But rather than use known perceptual models, we investigate the use of soft computing approaches classically used in the Artificial Intelligence (AI) areas such as full-reference and reduced-reference metrics.