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Objective video quality assessment (VQA) refers to evaluation of the quality of a video by an algorithm. The performance of any such VQA algorithm is gaged by how well the algorithmic scores correlate with human perception of quality. Research in the area of VQA has produced a host of full-reference (FR) VQA algorithms. FR VQA algorithms are those in which the algorithm has access to both the original reference video and the distorted video whose quality is being assessed. However, in many cases, the presence of the original reference video is not guaranteed. Hence, even though many FR VQA algorithms have been shown to correlate well with human perception of quality, their utility remains constrained. In this chapter, we analyze recently proposed reduced/no-reference (RR/NR) VQA algorithms. RR VQA algorithms are those in which some information about the reference video and/or the distorting medium is embedded in the video under test. NR VQA algorithms are expected to assess the quality of videos without any knowledge of the reference video or the distorting medium. The utility of RR/NR algorithms has prompted the Video Quality Experts Group (VQEG) to devote resources towards forming a RR/NR test group. In this chapter, we begin by discussing how performance of any VQA algorithm is evaluated. We introduce the popular VQEG Phase-I VQA dataset and comment on its drawbacks. New datasets which allow for objective evaluation of algorithms are then introduced. We then summarize some properties of the human visual system (HVS) that are frequently utilized in developing VQA algorithms. Further, we enumerate the paths that current RR/NR VQA algorithms take in order to evaluate visual quality. We enlist some considerations that VQA algorithms need to consider for HD videos. We then describe exemplar algorithms and elaborate on possible shortcomings of these algorithms. Finally, we suggest possible future research directions in the field of VQA and conclude this chapter. Anush K. Moorthy · Alan C. Bovik Dept. of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, USA e-mail: anushmoorthy@mail.utexas.edu, bovik@ece.utexas.edu 4 A.K. Moorthy and A.C. Bovik

Mammography is at present the best available technique for early detection of breast cancer. The most common breast abnormalities that may indicate breast cancer are masses and calcifications. In some cases, subtle signs that can also lead to a breast cancer diagnosis, such as architectural distortion and bilateral asymmetry, are present. Breast abnormalities are defined with wide range of features and may be easily missed or misinterpreted by radiologists while reading large amount of mammographic images provided in screening programs. To help radiologists provide an accurate diagnosis, a computer-aided detection (CADe) and computer-aided diagnosis (CADx) algorithms are being developed. CADe and CADx algorithms help reducing the number of false positives and they assist radiologists in deciding between follow up and biopsy. This chapter gives a survey of image processing algorithms that have been developed for detection of masses and calcifications. An overview of algorithms in each step (segmentation step, feature extraction step, feature selection step, classification step) of the mass detection algorithms is given. Wavelet detection methods and other recently proposed methods for calcification detection are presented. An overview of contrast enhancement and noise equalization methods is given as well as an overview of calcification classification algorithms.

In this paper we analyse the effects that JPEG and JPEG2000 compression have on subspace appearance-based face recognition algorithms. This is the first comprehensive study of standard JPEG2000 compression effects on face recognition, as well as an extension of existing experiments for JPEG compression. A wide range of bitrates (compression ratios) was used on probe images and results are reported for 12 different subspace face recognition algorithms. Effects of image compression on recognition performance are of interest in applications where image storage space and image transmission time are of critical importance. It will be shown that not only that compression does not deteriorate performance but it, in some cases, even improves it slightly. Some unexpected effects will be presented (like the ability of JPEG2000 to capture the information essential for recognizing changes caused by images taken later in time) and lines of further research suggested.