Author Description

Digital images play a significant role in representing useful information; however, they may get distorted while passing through several operational stages, such as image acquisition, compression, transmission, processing, and reconstruction. Similarly, during the transmission of images, some data may be lost and the quality of the image may degrade due to bandwidth limitation. The use of distorted images in any application compromises its performance; and therefore, the identification and quantification of degradation become key issues to address in restoring the distorted images. This book covers different quality assessment techniques for natural images. It further discusses image enhancement techniques that are based on estimated quality. This book presents image quality assessment techniques for different distortions, such as poor contrast, poor illumination, noise and artifacts in deblocked images. Also presented are quality-aware techniques for image enhancement for different distortions. Review and objective questions (with answers) for each chapter will be available online as part of the supplementary material. Contents: About the Authors Introduction The Human Visual System Image Quality Factors Review of Image Quality Assessment Databases Subjective and Objective Image Quality Assessment Full Reference Image Quality Assessment Reduced-Reference Image Quality Assessment No Reference Image Quality Assessment (NR-IQA) Quality Aware Image Enhancement Applications of Image Quality Assessment Challenges in Image Quality Assessment and Breakthroughs Emerging Trends and Future Directions Index Readership: Advanced undergraduate and graduate students, and researchers specialising in Artificial Intelligence, Computer Vision, Pattern Recognition and Image Analysis; practitioners in the fields of Image proc