This book is about objective image quality assessmentwhere 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.

Modern image quality assessment

Gray level co-occurrence matrix has proven to be a powerful basis for use in texture classification. Various textural parameters calculated from the gray level co-occurrence matrix help understand the details about the overall image content. The aim of this research is to investigate the use of the gray level co-occurrence matrix technique as an absolute image quality metric. The underlying hypothesis is that image quality can be determined by a comparative process in which a sequence of images is compared to each other to determine the point of diminishing returns. An attempt is made to study whether the curve of image textural features versus image memory sizes can be used to decide the optimal image size. The approach used digitized images that were stored at several levels of compression. GLCM proves to be a good discriminator in studying different images however no such claim can be made for image quality. Hence the search for the best image quality metric continues.

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Image Quality Analysis Using Glcm

Blind image quality assessment refers to the problem of evaluating the visual quality of an image without any reference. In this paper, we present a no-reference blur metric for images and video using information contained in the image itself. We look at the sharpness of the sharpest edges in the blurred image, which contains information about the blurring, and is not on the image contents. The experimental results are used to justify the validity of our approach.

Blind Image Quality Assessment for Measuring Image Blur

Our objectives were to provide an automated method for spatially resolved detection and quantification of motion artifacts in MR images of the head and abdomen as well as a quality control of the trained architecture. T1-weighted MR images of the head and the upper abdomen were acquired in 16 healthy volunteers under rest and under motion. Images were divided into overlapping patches of different sizes achieving spatial separation. Using these patches as input data, a convolutional neural network (CNN) was trained to derive probability maps for the presence of motion artifacts. A deep visualization offers a human-interpretable quality control of the trained CNN. Results were visually assessed on probability maps and as classification accuracy on a per-patch, per-slice and per-volunteer basis. On visual assessment, a clear difference of probability maps was observed between data sets with and without motion. The overall accuracy of motion detection on a per-patch/per-volunteer basis reached 97%/100% in the head and 75%/100% in the abdomen, respectively. Automated detection of motion artifacts in MRI is feasible with good accuracy in the head and abdomen. The proposed method provides quantification and localization of artifacts as well as a visualization of the learned content. It may be extended to other anatomic areas and used for quality assurance of MR images.

Automated reference-free detection of motion artifacts in magnetic resonance images.

Previous studies have evaluated the effect of the new still image compression standard JPEG 2000 using nontask based image quality metrics, i.e., peak-signal-to-noise-ratio (PSNR) for nonmedical images. In this paper, the effect of JPEG 2000 encoder options was investigated using the performance of human and model observers (nonprewhitening matched filter with an eye filter, square-window Hotelling, Laguerre-Gauss Hotelling and channelized Hotelling model observer) for clinically relevant visual tasks. Two tasks were investigated: the signal known exactly but variable task (SKEV) and the signal known statistically task (SKS). Test images consisted of real X-ray coronary angiograms with simulated filling defects (signals) inserted in one of the four simulated arteries. The signals varied in size and shape. Experimental results indicated that the dependence of task performance on the JPEG 2000 encoder options was similar for all model and human observers. Model observer performance in the more tractable and computationally economic SKEV task can be used to reliably estimate performance in the complex but clinically more realistic SKS task. JPEG 2000 encoder settings different from the default ones resulted in greatly improved model and human observer performance in the studied clinically relevant visual tasks using real angiography backgrounds.

Evaluation of JPEG 2000 encoder options: human and model observer detection of variable signals in X-ray coronary angiograms

This paper describes a multistage perceptual quality assessment (MPQA) model for compressed images. The motivation for the development of a perceptual quality assessment is to measure (in)visible differences between original and processed images. The MPQA produces visible distortion maps and quantitative error measures informed by considerations of the human visual system (HVS). Original and decompressed images are decomposed into different spatial frequency bands and orientations modeling the human cortex. Contrast errors are calculated for each frequency and orientation, and masked as a function of contrast sensitivity and background uncertainty. Spatially masked contrast error measurements are then made across frequency bands and orientations to produce a single perceptual distortion visibility map (PDVM). A perceptual quality rating (PQR) is calculated from the PDVM and transformed into a one to five scale, PQR/sub 1-5/, for direct comparison with the mean opinion score, generally used in subjective ratings. The proposed MPQA model is based on existing perceptual quality assessment models, while it is differentiated by the inclusion of contrast masking as a function of background uncertainty. A pilot study of clinical experiments on wavelet-compressed digital angiogram has been performed on a sample set of angiogram images to identify diagnostically acceptable reconstruction. Our results show that the PQR/sub 1-5/ of diagnostically acceptable lossy image reconstructions have better agreement with cardiologists' responses than objective error measurement methods, such as peak signal-to-noise ratio. A Perceptual thresholding and CSF-based Uniform quantization (PCU) method is also proposed using the vision models presented in this paper. The vision models are implemented in the thresholding and quantization stages of a compression algorithm and shown to produce improved compression ratio performance with less visible distortion than that of the embedded zerotrees wavelet (EZWs).

A multistage perceptual quality assessment for compressed digital angiogram images

In this paper we discuss the requirements of medical image compression methods and present results from diagnostic quality tests of lossily wavelet-compressed digital angiogram images.

http://ieeexplore.ieee.org/iel5/6416/17139/00791101.pdf

Diagnostic quality test for wavelet-compressed digital angiogram images

The choice of filter bank in wavelet compression is an important issue that affects image quality. This paper presents the results of an on-going research project into digital angiogram compression. Several reversible biorthogonal wavelet transforms with perfect reconstruction and linear phase are applied to digital angiograms and their performances are compared. It is found that biorthogonal wavelet transforms produce higher compression ratios than orthogonal transforms with similar image quality.

Reversible wavelet compression for digital angiograms

This paper investigates the effects of lossless and lossy wavelet image compression methods on digital angiogram images. The requirements of suitable quality assessment methods for decompressed images are considered and the need for diagnostically relevant quality metrics in discussed. The results of experimental quality investigations, including expert cardiologists identifications of diagnostically acceptable quality boundaries, are presented and discussed.

Quality assessment methods for wavelet-compressed digital angiogram images

The choice presents the results of wavelet transforms i performances are com orthogonal transforms ? filter bank in wavelet compression is an important issue that affects image quality. This paper n on-going research project into digital angiogram compression. Several reversible biorthogonal ith perfect reconstruction and linear phase are applied to digital angiograms and their iared. It is found that biorthogonal wavelet transforms produce higher compression ratios than with similar image quality. mpression, biorthogonal wavelet transformation, digital angiograms.

http://ieeexplore.ieee.org/iel4/6018/16077/00747155.pdf

Joonmi Oh

Papers

A multistage perceptual quality assessment for compressed digital angiogram images

Diagnostic quality test for wavelet-compressed digital angiogram images

Reversible wavelet compression for digital angiograms

Quality assessment methods for wavelet-compressed digital angiogram images

E wavelet compression for digital angiograms