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166
Multi-Scale Structural Similarity for Image Quality Assessment
- in Proc. IEEE Asilomar Conf. on Signals, Systems, and Computers, (Asilomar
, 2003
"... The structural similarity image quality paradigm is based on the assumption that the human visual system is highly adapted for extracting structural information from the scene, and therefore a measure of structural similarity can provide a good approximation to perceived image quality. This paper pr ..."
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Cited by 237 (39 self)
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The structural similarity image quality paradigm is based on the assumption that the human visual system is highly adapted for extracting structural information from the scene, and therefore a measure of structural similarity can provide a good approximation to perceived image quality. This paper proposes a multi-scale structural similarity method, which supplies more flexibility than previous single-scale methods in incorporating the variations of viewing conditions. We develop an image synthesis method to calibrate the parameters that define the relative importance of different scales. Experimental comparisons demonstrate the effectiveness of the proposed method.
Study of subjective and objective quality assessment of video
- IEEE Trans. Image Process
, 2010
"... Abstract—We present the results of a recent large-scale sub-jective study of video quality on a collection of videos distorted by a variety of application-relevant processes. Methods to as-sess the visual quality of digital videos as perceived by human observers are becoming increasingly important, ..."
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Cited by 75 (18 self)
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Abstract—We present the results of a recent large-scale sub-jective study of video quality on a collection of videos distorted by a variety of application-relevant processes. Methods to as-sess the visual quality of digital videos as perceived by human observers are becoming increasingly important, due to the large number of applications that target humans as the end users of video. Owing to the many approaches to video quality assessment (VQA) that are being developed, there is a need for a diverse independent public database of distorted videos and subjective scores that is freely available. The resulting Laboratory for Image and Video Engineering (LIVE) Video Quality Database contains 150 distorted videos (obtained from ten uncompressed reference videos of natural scenes) that were created using four different commonly encountered distortion types. Each video was assessed by 38 human subjects, and the difference mean opinion scores (DMOS) were recorded. We also evaluated the performance of several state-of-the-art, publicly available full-reference VQA algorithms on the new database. A statistical evaluation of the relative performance of these algorithms is also presented. The database has a dedicated web presence that will be maintained as long as it remains relevant and the data is available online. Index Terms—Full reference, human visual system, LIVE video quality database, perceptual quality assessment, video quality, vi-sual perception.
Motion Tuned Spatio-temporal Quality Assessment of Natural Videos
- IEEE TRANSACTIONS ON IMAGE PROCESSING
, 2010
"... There has recently been a great deal of interest in the development of algorithms that objectively measure the integrity of video signals. Since video signals are being delivered to human end users in an increasingly wide array of applications and products, it is important that automatic methods of ..."
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Cited by 74 (7 self)
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There has recently been a great deal of interest in the development of algorithms that objectively measure the integrity of video signals. Since video signals are being delivered to human end users in an increasingly wide array of applications and products, it is important that automatic methods of video quality assessment (VQA) be available that can assist in controlling the quality of video being delivered to this critical audience. Naturally, the quality of motion representation in videos plays an important role in the perception of video quality, yet existing VQA algorithms make little direct use of motion information, thus limiting their effectiveness. We seek to ameliorate this by developing a general, spatio-spectrally localized multiscale framework for evaluating dynamic video fidelity that integrates both spatial and temporal (and spatio-temporal) aspects of distortion assessment. Video quality is evaluated not only in space and time, but also in space-time, by evaluating motion quality along computed motion trajectories. Using this framework, we develop a full reference VQA algorithm for which we coin the term the MOtion-based Video Integrity Evaluation index, or MOVIE index. It is found that the MOVIE index delivers VQA scores that correlate quite closely with human subjective judgment, using the Video Quality Expert Group (VQEG) FRTV Phase 1 database as a test bed. Indeed, the MOVIE index is found to be quite competitive with, and even outperform, algorithms developed and submitted to the VQEG FRTV Phase 1 study, as well as more recent VQA algorithms tested on this database.
Information content weighting for perceptual image quality assessment
- IEEE Trans. Image Processing
, 2011
"... Abstract—Many state-of-the-art perceptual image quality as-sessment (IQA) algorithms share a common two-stage structure: local quality/distortion measurement followed by pooling. While significant progress has been made in measuring local image quality/distortion, the pooling stage is often done in ..."
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Cited by 71 (16 self)
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Abstract—Many state-of-the-art perceptual image quality as-sessment (IQA) algorithms share a common two-stage structure: local quality/distortion measurement followed by pooling. While significant progress has been made in measuring local image quality/distortion, the pooling stage is often done in ad-hoc ways, lacking theoretical principles and reliable computational models. This paper aims to test the hypothesis that when viewing natural images, the optimal perceptual weights for pooling should be proportional to local information content, which can be estimated in units of bit using advanced statistical models of natural images. Our extensive studies based upon six publicly-available sub-ject-rated image databases concluded with three useful findings. First, information content weighting leads to consistent improve-ment in the performance of IQA algorithms. Second, surprisingly, with information content weighting, even the widely criticized peak signal-to-noise-ratio can be converted to a competitive perceptual quality measure when compared with state-of-the-art algorithms. Third, the best overall performance is achieved by combining information content weighting with multiscale struc-tural similarity measures. Index Terms—Gaussian scale mixture (GSM), image quality assessment (IQA), pooling, information content measure, peak signal-to-noise-ratio (PSNR), structural similarity (SSIM), statis-tical image modeling. I.
The Evolution of Video Quality Measurement: From PSNR to Hybrid Metrics
, 200
"... This paper reviews the evolution of video quality measurement techniques and their current state of the art. We start with subjective experiments and then discuss the various types of objective metrics and their uses. We also introduce V-Factor, a “hybrid” metric using both transport- and bitstream ..."
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Cited by 54 (6 self)
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This paper reviews the evolution of video quality measurement techniques and their current state of the art. We start with subjective experiments and then discuss the various types of objective metrics and their uses. We also introduce V-Factor, a “hybrid” metric using both transport- and bitstream information. Finally, we summarize the main standardization activities, such as the work of the Video Quality Experts Group (VQEG), and we take a look at emerging trends in quality measurement, including image preference, visual attention, and audiovisual quality.
Video Quality Assessment Using a Statistical Model of Human Visual Speed Perception
"... Motion is one of the most important types of information contained in natu-ral video, but direct use of motion information in the design of video quality assessment algorithms has not been deeply investigated. Here we propose to in-corporate a recent model of human visual speed perception [Stocker & ..."
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Cited by 52 (7 self)
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Motion is one of the most important types of information contained in natu-ral video, but direct use of motion information in the design of video quality assessment algorithms has not been deeply investigated. Here we propose to in-corporate a recent model of human visual speed perception [Stocker & Simon-celli, Nature Neuroscience 9, 578-585 (2006)] and model visual perception in an information communication framework. This allows us to estimate both the motion information content and the perceptual uncertainty in video signals. Improved video quality assessment algorithms are obtained by incorporating the model as spatiotemporal weighting factors, where the weight increases with the information content and decreases with the perceptual uncertainty. Consistent improvement over existing video quality assessment algorithms is observed in our validation with the video quality experts group Phase I test data set.
3d-ssim for video quality assessment
- in Image Processing (ICIP), 2012 19th IEEE International Conference on
, 2012
"... Effective and efficient objective video quality assessment (VQA) methods are highly desirable in modern visual communication sys-tems for performance evaluation, quality control and resource alloca-tion purposes. Simple VQA algorithms may be developed by direct extensions of still image quality asse ..."
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Cited by 49 (16 self)
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Effective and efficient objective video quality assessment (VQA) methods are highly desirable in modern visual communication sys-tems for performance evaluation, quality control and resource alloca-tion purposes. Simple VQA algorithms may be developed by direct extensions of still image quality assessment (IQA) approaches on a frame-by-frame basis. Advanced VQA methods take into account the temporal correlation and motion information contained in video signals but often lead to significantly increased computational com-plexity. Here we use a different approach to examine a video signal by considering it as a three-dimensional (3D) volume image. Specif-ically, we propose a 3D structural similarity (3D-SSIM) approach, which first creates a 3D quality map by applying SSIM evaluations within local 3D blocks, and then use local information content and local distortion based weighting methods to pool the quality map into a single quality measure. The resulting 3D-SSIM algorithm is computationally efficient and demonstrates highly competitive per-formance in comparison with state-of-the-art VQA algorithms when tested using four publicly available video quality databases1. Index Terms — video quality assessment, structural similarity, 3D volume image quality assessment, information content weighting 1.
Mean Squared Error: Love it or leave it?
, 2009
"... For more than 50 years, the meansquared error (MSE) has been the dominant quantitative performance metric in the field of signal processing. It remains the standard criterion for the assessment of signal quality and fidelity; it is the method of choice for comparing competing signal processing metho ..."
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Cited by 32 (0 self)
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For more than 50 years, the meansquared error (MSE) has been the dominant quantitative performance metric in the field of signal processing. It remains the standard criterion for the assessment of signal quality and fidelity; it is the method of choice for comparing competing signal processing methods and systems, and, perhaps most importantly, it is the nearly ubiquitous preference of design engineers seeking to optimize signal processing algorithms. This is true despite the fact that in many of these applications, the MSE exhibits weak performance and has been widely criticized for serious shortcomings, especially when dealing with perceptually important signals such as speech and images. Yet the MSE has exhibited remarkable staying power, and prevailing attitudes towards the MSE seem to range from “it’s easy to use and not so bad ” to “everyone else uses it.” So what is the secret of the MSE—why is it still so popular? And is this popularity misplaced? What is wrong with the MSE when it does not work well? Just how wrong is the MSE in these cases? If not the MSE, what else can be used? These are the questions we’ll be concerned with in this article. Our backgrounds are primarily in the field of image processing, where the MSE has a particularly bad reputation, but where, ironically, it is used nearly as much as in other areas of signal processing. Our discussion will often deal with the role of the MSE (and alternative methods) for processing visual signals. Owing to the poor performance of the MSE as a visual metric, interesting alternatives are arising in the image processing field. Our goal is to stimulate fruitful thought and discussion regarding the role of the MSE in processing other types of signals. More specifically, we hope to inspire signal processing engineers to rethink whether the MSE is truly the criterion of choice in their own theories and applications, and whether it is time to look for alternatives.
Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison
"... Abstract—With the increasing demand for video-based applications, the reliable prediction of video quality has increased in importance. Numerous video quality assessment methods and metrics have been proposed over the past years with varying computational complexity and accuracy. In this paper, we i ..."
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Cited by 29 (2 self)
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Abstract—With the increasing demand for video-based applications, the reliable prediction of video quality has increased in importance. Numerous video quality assessment methods and metrics have been proposed over the past years with varying computational complexity and accuracy. In this paper, we introduce a classification scheme for full-reference and reduced-reference media-layer objective video quality assessment methods. Our classification scheme first classifies a method according to whether natural visual characteristics or perceptual (human visual system) characteristics are considered. We further subclassify natural visual characteristics methods into methods based on natural visual statistics or natural visual features. We subclassify perceptual characteristics methods into frequency- or pixel-domain methods. According to our classification scheme, we comprehensively review and compare the media-layer objective video quality models for both standard resolution and high definition video. We find that the natural visual statistics based MultiScale-Structural SIMilarity index (MS-SSIM), the natural visual feature based Video Quality Metric (VQM), and the perceptual spatio-temporal frequency-domain based MOtion-based Video Integrity Evaluation (MOVIE) index give the best performance for the LIVE Video
Spatio-temporal transform based video hashing
- IEEE Transactions on Multimedia
, 2006
"... Abstract—Identification and verification of a video clip via its fingerprint find applications in video browsing, database search and security. For this purpose, the video sequence must be collapsed into a short fingerprint using a robust hash function based on signal processing operations. We propo ..."
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Cited by 24 (0 self)
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Abstract—Identification and verification of a video clip via its fingerprint find applications in video browsing, database search and security. For this purpose, the video sequence must be collapsed into a short fingerprint using a robust hash function based on signal processing operations. We propose two robust hash algorithms for video based both on the Discrete Cosine Transform (DCT), one on the classical basis set and the other on a novel randomized basis set (RBT). The robustness and randomness properties of the proposed hash functions are investigated in detail. It is found that these hash functions are resistant to signal processing and transmission impairments, and therefore can be instrumental in building database search, broadcast monitoring and watermarking applications for video. The DCT hash is more robust, but lacks security aspect, as it is easy to find different video clips with the same hash value. The RBT based hash, being secret key based, does not allow this and is more secure at the cost of a slight loss in the receiver operating curves. Index Terms—Broadcast monitoring, multimedia content authentication, robust hash, video database indexing, video hash. I.
