Modern Image Quality Assessment
Morgan & Claypool Publishers, 2006 - Technology & Engineering - 146 pages
This Lecture book is about objective image quality assessment--where the aim is to provide computational models that can automatically predict perceptual image quality. The early years of the 21st century have witnessed a tremendous growth in the use of digital images as a means for representing and communicating information. A considerable percentage of this literature is devoted to methods for improving the appearance of images, or for maintaining the appearance of images that are processed. Nevertheless, the quality of digital images, processed or otherwise, is rarely perfect. Images are subject to distortions during acquisition, compression, transmission, processing, and reproduction. To maintain, control, and enhance the quality of images, it is important for image acquisition, management, communication, and processing systems to be able to identify and quantify image quality degradations. The goals of this book are as follows; a) to introduce the fundamentals of image quality assessment, and to explain the relevant engineering problems, b) to give a broad treatment of the current state-of-the-art in image quality assessment, by describing leading algorithms that address these engineering problems, and c) to provide new directions for future research, by introducing recent models and paradigms that significantly differ from those used in the past. The book is written to be accessible to university students curious about the state-of-the-art of image quality assessment, expert industrial R&D engineers seeking to implement image/video quality assessment systems for specific applications, and academic theorists interested in developing new algorithms for image quality assessment or using existing algorithms to design or optimize other image processing applications.
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application-speciﬁc applications block-based blurring complex wavelet transform computed contrast CW-SSIM Index deﬁned described difﬁcult distorted image distortion channel distortion measure distortion types domain method efﬁcient error signal evaluation factor feature extraction ﬁdelity ﬁeld of image FIGURE ﬁlter ﬁrst frequency domain function ganglion cells histogram human visual image compression image distortions image quality assessment image quality measures image space image-processing implementation joint histogram JPEG JPEG compressed image lp norms luminance marginal distributions masking mutual information natural image statistics normalization original image overall parameters pixels problem processing psychophysical quality assessment algorithms quality assessment systems quality-aware image quantization receiver side reference and distorted reference image RR features RR quality assessment samples scale shown in Fig side information signiﬁcantly spatial domain speciﬁc structural similarity subband test image types of distortions vector VIF Index visual perception wavelet coefﬁcients wavelet domain wavelet transform white Gaussian noise
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