Objective methods for assessing perceptual image quality have traditionally attempted to quantify the visibility of errors 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 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.

Objective methods for assessing perceptual image quality traditionally attempt to quantify the visibility of errors (di#erences) 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/~lcv/ssim/.

This paper deals with the optimal allocation of MPEG-2 encoding and media-independent forward error correction (FEC) rates under a total given bandwidth. The optimality is defined in terms of minimum perceptual distortion given a set of video and network parameters. We first derive the set of equations leading to the residual loss process parameters. That is, the packet loss ratio (PLR) and the average burst length after FEC decoding. We then show that the perceptual source distortion decreases exponentially with the increasing MPEG-2 source rate. We also demonstrate that the perceptual distortion due to data loss is directly proportional to the number of lost macroblocks, and therefore decreases with the amount of channel protection. Finally, we derive the global set of equations that lead to the optimal dynamic rate allocation. The optimal distribution is shown to outperform classical FEC scheme, thanks to its adaptivity to the scene complexity, the available bandwidth and to the network performance. Furthermore, our approach holds for any standard video compression algorithms (i.e., MPEG-x, H.26x).

Measurement of visual quality is of fundamental importance to numerous image and video processing applications. The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images or videos in a perceptually consistent manner. Traditionally, image QA algorithms interpret image quality as fidelity or similarity with a “reference” or “perfect” image in some perceptual space. Such “full-referenc” QA methods attempt to achieve consistency in quality prediction by modeling salient physiological and psychovisual features of the human visual system (HVS), or by arbitrary signal fidelity criteria. In this paper, we approach the problem of image QA by proposing a novel information fidelity criterion that is based on natural scene statistics. QA systems are invariably involved with judging the visual quality of images and videos that are meant for “human consumption. ” Researchers have developed sophisticated models to capture the statistics of natural signals, that is, pictures and videos of the visual environment. Using these statistical models in an information-theoretic setting, we derive a novel QA algorithm that provides clear advantages over the traditional approaches. In particular, it is parameterless and outperforms current methods in our testing. We validate the performance of our algorithm with an extensive subjective study involving 779 images. We also show that, although our approach distinctly departs from traditional HVS-based methods, it is functionally similar to them under certain conditions, yet it outperforms them due to improved modeling. The code and the data from the subjective study are available at [1].

Guaranteeing a certain level of quality for multimedia streaming applications is quite well understood in terms of network QoS, but it is much more tenuous in terms of perceptual quality as perceived by the user. In this paper, we classify video quality measurement schemes and review existing approaches with a focus on non-intrusive quality metrics, which do not require access to the reference video. In particular, we evaluate three different no-reference blockiness metrics and compare their performance. Keywords Perceptual QoS, visual quality assessment, blocking artifact, no-reference blockiness metrics 1.

Working with contrast instead of luminance can facilitate numerous image processing and analysis tasks. Unfortunately, a common definition of contrast suitable for all situations does not exist. In this paper we review existing contrast definitions for natural images and propose a new isotropic contrast measure, which is computed from oriented filters. We investigate some of its properties and apply it to natural images.

The design of reliable visual quality metrics is complicated by our limited knowledge of the human visual system and the resulting variety of pertinent vision models. We have begun to analyze and compare a number of implementation choices for some components found in most of today's visual quality metrics that are based on a model of human vision and present the first results here. Keywords: Vision model evaluation, video quality assessment, perceptual distortion metric 1. INTRODUCTION The introduction of digital TV systems on the consumer market has lead to a rising demand for pertinent measurement tools, not only on the bitstream and network level, but also for video quality assessment on a perceptual level. Consequently, considerable effort has been put in the development of visual quality metrics in recent years, many of which rely on models of the human visual system (an overview of current modeling approaches was presented elsewhere by the author 14 ). As a matter of fact, t...

Assessing and comparing the performance of watermarking algorithms is difficult. The visibility of the watermark is an important aspect in this process. In this paper, we propose two metrics for evaluating the visual impact of video watermarks. Based on several different watermarking algorithms and video sequences, we identify the most prominent impairments as spatial noise and temporal flicker. We design the corresponding measurement algorithms and corroborate their performance through subjective experiments. 1.

this report we introduce the VideoMeter, a tool developed for the comparative evaluation of the quality of raw video data in the YUV format. The tool gives the di#erences in PSNR quality between two or three YUV video sequences and includes also a player for YUV video streams. In a typical application scenario, the tool is used for the quality assessment of videos that have been encoded with some lossy compression scheme and transported over a lossy network. The tool can be used to simultaneously play (i) the original YUV video sequence, (ii) the encoded (and subsequently decoded) video sequence, and (iii) the video sequence obtained after encoding, network transport, and subsequent decoding. The tool gives the quality di#erences in PSNR between the original video sequence and the video sequences obtained after encoding and network transport

In this paper, a novel learning based method is proposed for No-Reference image quality assessment. Instead of examining the exact prior knowledge for the given type of distortion and finding a suitable way to represent it, our method aims to directly get the quality metric by means of learning. At first, some training examples are prepared for both high-quality and lowquality classes; then a binary classifier is built on the training set; finally the quality metric of an un-labeled example is denoted by the extent to which it belongs to these two classes. Different schemes to acquire examples from a given image, to build the binary classifier and to model the quality metric are proposed and investigated. While most existing methods are tailored for some specific distortion type, the proposed method might provide a general solution for No-Reference image quality assessment. Experimental results on JPEG and JPEG2000 compressed images validate the effectiveness of the proposed method. 1.