Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.

/pdf/image-quality-assessment-from-error-visibility-to-structural-1rlwcqe34t.pdf

Image quality assessment: from error visibility to structural similarity

The National Telecommunications and Information Administration (NTIA) General Model for estimating video quality and its associated calibration techniques were independently evaluated by the Video Quality Experts Group (VQEG) in their Phase II Full Reference Television (FR-TV) test. The NTIA General Model was the only video quality estimator that was in the top performing group for both the 525-line and 625-line video tests. As a result, the American National Standards Institute (ANSI) adopted the NTIA General Model and its associated calibration techniques as a North American Standard in 2003. The International Telecommunication Union (ITU) has also included the NTIA General Model as a normative method in two Draft Recommendations. This paper presents a description of the NTIA General Model and its associated calibration techniques. The independent test results from the VQEG FR-TV Phase II tests are summarized, as well as results from eleven other subjective data sets that were used to develop the method.

A new standardized method for objectively measuring video quality

Objective image and video quality measures play important roles in a variety of image and video pro- cessing applications, such as compression, communication, printing, analysis, registration, restoration, enhancement and watermarking. Most proposed quality assessment ap- proaches in the literature are error sensitivity-based meth- ods. In this paper, we follow a new philosophy in designing image and video quality metrics, which uses structural dis- tortion as an estimate of perceived visual distortion. A com- putationally ecient approach is developed for full-reference (FR) video quality assessment. The algorithm is tested on the video quality experts group (VQEG) Phase I FR-TV test data set. Keywords—Image quality assessment, video quality assess- ment, human visual system, error sensitivity, structural dis- tortion, video quality experts group (VQEG)

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Video Quality Assessment Based on Structural Distortion Measurement

Objective methods for assessing perceptual im- age quality traditionally attempt to quantify the visibility of errors (dierences) between a distorted image and a ref- erence image using a variety of known properties of the hu- man visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative com- plementary framework for quality assessment based on the degradation of structural information. As a specific exam- ple of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive ex- amples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MatLab imple- mentation of the proposed algorithm is available online at http://www.cns.nyu.edu/~lcv/ssim/.

Image Quality Assessment: From Error Measurement to Structural Similarity

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

The growth of digital video has given rise to a need for computational methods for evaluating the visual quality of digital video. We have developed a new digital video quality metric, which we call DVQ (Digital Video Quality) 1 . Here we provide a brief description of the metric, and give a preliminary report on its performance. DVQ accepts a pair of digital video sequences, and computes a measure of the magnitude of the visible difference between them. The metric is based on the Discrete Cosine Transform. It incorporates aspects of early visual processing, including light adaptation, luminance and chromatic channels, spatial and temporal filtering, spatial frequency channels, contrast masking, and probability summation. It also includes primitive dynamics of light adaptation and contrast masking. We have applied the metric to digital video sequences corrupted by various typical compression artifacts, and compared the results to quality ratings made by human observers.

/pdf/dvq-a-digital-video-quality-metric-based-on-human-vision-3kyixwbmkm.pdf

DVQ: A digital video quality metric based on human vision

The growth of digital video has given rise to a need for computational methods for evaluating the visual quality of digital video. We have developed a new digital video quality metric, which we call DVQ (digital video quality) (A. B. Watson, in Human Vision, Visual Processing, and Digital Display VIII, Proc. SPIE 3299, 139- 147 (1998)). Here, we provide a brief description of the metric, and give a preliminary report on its performance. DVQ accepts a pair of digital video sequences, and computes a measure of the magnitude of the visible difference between them. The metric is based on the discrete cosine transform. It incorporates aspects of early visual pro- cessing, including light adaptation, luminance, and chromatic chan- nels; spatial and temporal filtering; spatial frequency channels; con- trast masking; and probability summation. It also includes primitive dynamics of light adaptation and contrast masking. We have applied the metric to digital video sequences corrupted by various typical compression artifacts, and compared the results to quality ratings made by human observers. © 2001 SPIE and IS&T. (DOI: 10.1117/1.1329896)

Digital video quality metric based on human vision

The growth of digital video has given rise to a need for computational methods for evaluating the visual quality of digital video. We have developed a new digital video quality metric, which we call DVQ. Here we provide a brief description of the metric, and give a preliminary report on its performance. DVQ accepts a pair of digital video sequences, and computes a measure of the magnitude of the visible difference between them. The metric is based on the Discrete Cosine Transform. It incorporates aspects of early visual processing, including light adaptation, luminance and chromatic channels, spatial and temporal filtering, spatial frequency channels, contrast masking, and probability summation. It also includes primitive dynamics of light adaptation and contrast masking. We have applied the metric to digital video sequences corrupted by various typical compression artifacts, and compared the results to quality ratings made by human observers.

Design and performance of a digital video quality metric

On Earth, according to conventional theory, the largest, by mass and volume, identifiable trace of past life is subsurface oil and natural gas deposits. Nearly all coal and oil on Earth and most sedimentary source rocks associated with coal, oil, and natural gas contain molecules of biological origin and is proof of past life. If Mars possessed an Earth-like biosphere in the past, Mars may contain subsurface deposits of oil and natural gas indicating past life. Life might still exist in these deposits. Subsurface oil and natural gas on Mars would probably cause seepage of hydrocarbon gases such as methane at favorable locations on the Martian surface. Further, if Mars contains substantial subsurface life, the most detectable signature of this life on the Martian surface would be gases generated by the life percolating up to the surface and venting into the Martian atmosphere. In this paper, systems that can detect evidence of subsurface oil and gas, including ground penetrating radar and infrared gas sensors are explored. The limitations and future prospects of infrared gas detection and imaging technologies are explored. The power, mass, and volume requirements for infrared instruments able to detect venting gases, especially methane, from an aerobot is estimated. The maximum range from the infrared sensor to the gas vent and the minimum detectable gas density or fraction of the Martian atmosphere - as appropriate for the instrument type - is estimated. The bit rate and bit error rate requirements for transmitting the data back to Earth are also estimated.

Oil and natural gas on Mars

Eye movement desensitization and reprocessing (EMDR) therapy has a rapidly growing evidence base; however, research into its changes in attachment security during EMDR therapy is limited. This pilot study aimed to explore changes in attachment security in a clinical sample of adults who received EMDR therapy for symptoms of posttraumatic stress disorder (PTSD) and complex posttraumatic stress disorder (CPTSD). It also explored the quality of the therapeutic alliance in relation to changes in attachment security. A within-subject, repeated-measures design was used. Eighteen participants received fifteen EMDR sessions on average and completed self-report measures of attachment, PTSD, CPTSD, and therapeutic alliance. A decrease in attachment insecurity was observed. Changes in attachment security were partially associated with the quality of the therapeutic alliance and changes in symptomatology. This study contributes to the emerging literature on change in attachment and EMDR therapy. 

John F. McGowan

Papers

DVQ: A digital video quality metric based on human vision

Digital video quality metric based on human vision

Design and performance of a digital video quality metric

Oil and natural gas on Mars

Eye Movement Desensitization and Reprocessing Therapy and Change in Attachment Security: A Pilot Study