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Visual Quality Assessment by Machine Learning

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSSIGNAL))

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Abstract

With the development of digital visual signal processing, efficient and reliable assessment of image quality becomes more and more important. Measuring the image quality is of fundamental importance for image processing applications, where the goal of image quality assessment (IQA) methods is to automatically evaluate the quality of images in agreement with human visual quality judgments. In the past decade, researchers have made great efforts to develop many IQAs to fulfill this goal. According to the availability of reference, these IQAs can be classified into full-reference, reduce-reference, and no-reference IQAs. For most of the applications, there is no reference available during visual signal processing, e.g., decoding of compressed visual signal in client’s terminal without the original signal stored in server. The absence of reference raises a great challenge for visual quality assessment (VQA). To address this challenge, machine learning was widely used to approximate the response of the human visual system (HVS) to visual quality perception. This chapter will present the fundamental knowledge of VQA, overview of the state-of-the-art IQAs in the literature, resource of VQA, standards developed for subjective quality assessment, and summary of all related items.

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Authors and Affiliations

  1. National Astronomical Observatories, Chinese Academy of Sciences, Beijing, China

    Long Xu

  2. Nanyang Technological University, Singapore, Singapore

    Weisi Lin

  3. University of Southern California, Los Angeles, CA, USA

    C.-C. Jay Kuo

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  1. Long Xu
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Xu, L., Lin, W., Kuo, CC.J. (2015). Introduction. In: Visual Quality Assessment by Machine Learning. SpringerBriefs in Electrical and Computer Engineering(). Springer, Singapore. https://doi.org/10.1007/978-981-287-468-9_1

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