This paper presents an extensible and reusable framework which addresses the problem of video quality assessment over IP networks. The proposed tool (referred to as Video-Tester) supports raw uncompressed video encoding and decoding. It also includes different video over IP transmission methods (i.e.: RTP over UDP unicast and multicast, as well as RTP over TCP). In addition, it is furnished with a rich set of offline analysis capabilities. Video-Tester analysis includes QoS and bitstream parameters estimation (i.e.: bandwidth, packet inter-arrival time, jitter and loss rate, as well as GOP size and I-frame loss rate). Our design facilitates the integration of virtually any existing video quality metric thanks to the adopted Python-based modular approach. Video-Tester currently provides PSNR, SSIM, ITU-T G.1070 video quality metric, DIV and PSNR-based MOS estimations. In order to promote its use and extension, Video-Tester is open and publicly available.
Several quality evaluation toolboxes have been recently proposed, like the implemented by Murthy and Karam @cite_9 . However, in general, these applications are mainly restricted to codec testing and metric comparisons, because they do not consider multimedia transmission methods and the degradation and quality impact derived from this process.
@cite_9
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This paper proposes a no-reference quality assessment metric for images subject to quantization noise in block-based DCT (discrete cosine transform) domain, as those resulting from JPEG or MPEG encoding. The proposed method is based on natural scene statistics of the DCT coefficients, whose distribution is usually modeled by a Laplace probability density function, with parameter @l. A new method for @l estimation from quantized coefficient data is presented; it combines maximum-likelihood with linear prediction estimates, exploring the correlation between @l values at adjacent DCT frequencies. The resulting coefficient distributions are then used for estimating the local error due to lossy encoding. Local error estimates are also perceptually weighted, using a well-known perceptual model by Watson. When confronted with subjective quality evaluation data, results show that the quality scores that result from the proposed algorithm are well correlated with the human perception of quality. Since no knowledge about the original (reference) images is required, the proposed method resembles a no-reference quality metric for image evaluation. A major fraction of multimedia stream contents tends to be redundant and leads to wastage of storage capacity and channel bandwidth. In order to eliminate surplus data, standard video compression algorithms exploit spatial and temporal correlation present in video sequence. However, in case of a multisensor network, intersensor statistical redundancy is the most significant factor in acquiring efficient link utilization as well as making resultant findings valuable to the end user. In this paper, an extension to our previously proposed scheme has been presented to accomplish performance goals of a multisensor environment. Standard MPEG codec has been used to accomplish distributed motion compensation in prespecified directions known as directional correlation. Video frame correlation has been estimated locally at the camera node as well as across different nodes, defined as node communication strategies. Further, receiver feedback assists in quality control after reconstitution by decoder assessment. Results estimated have been analyzed for saving ratios and multimedia quality. Results analysis illustrates increased gains in frame quality and compression saving, achieved through reducing node displacement from the reference node (NR). It is generally recognized that severe video distortions that are transient in space and or time have a large effect on overall perceived video quality. In order to understand this phenomena, we study the distribution of spatio-temporally local quality scores obtained from several video quality assessment (VQA) algorithms on videos suffering from compression and lossy transmission over communication channels. We propose a content adaptive spatial and temporal pooling strategy based on the observed distribution. Our method adaptively emphasizes “worst” scores along both the spatial and temporal dimensions of a video sequence and also considers the perceptual effect of large-area cohesive motion flow such as egomotion. We demonstrate the efficacy of the method by testing it using three different VQA algorithms on the LIVE Video Quality database and the EPFL-PoliMI video quality database. 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. 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 Quality Database. Mobile wireless multimedia applications (e.g., video calls and IPTV) have gained great momentum in recent years. An important issue is to monitor predict overall audiovisual quality, instead of audio-only or video-only quality, non-intrusively for technical or commercial reasons. Previous audiovisual modeling research mainly considered application parameters (e.g., codec and send bit rate). Little attention has been paid to how network parameters, e.g., Packet Error Rate (PER) affect audiovisual quality. The aim of this paper is to explore methods to predict audiovisual quality objectively for video calls in wireless applications. The contributions of the paper are twofold. Firstly, we present subjective test results on how audio and video contribute to overall audiovisual quality and develop models to reflect this relationship. Secondly, we investigated how network parameters (e.g., PER) and application parameters, e.g., video Frame Rate (FR) affect overall audiovisual quality. We developed a regression model to predict audiovisual quality from PER and FR which can be used to monitor predict audiovisual quality non-intrusively. We also explore the possibility to predict audiovisual quality from full-reference voice and video quality metrics (i.e., PESQ and PSNR) and from combined PESQ PSNR and network application parameters. The different predication accuracy obtained from these models (accuracy from 84 to 93 ) indicates the complex attributes in audiovisual quality prediction. An extended Evalvid NS-2 platform is developed to support simulation of video calls over wireless networks. This paper considers the use of the image quality metric for still image compression systems comparison. Considering the fact that the conventional PSNR cannot sufficiently reflect the result of subjective assessment other quality measures have been considered to design the variable bit-rate coders. Distortion measures are developed for the purpose of predicting the subjective quality of pictures. Subjectively relevant distortion measures that mirror viewers' assessments of picture quality would make the task of designing and optimizing coding schemes considerably easier. One significant problem associated with these measures is that there is little information on how these metrics perform in comparison to each other. The purpose of this paper is to provide a rigorous evaluation of three metrics to assess the quality of compressed images. The compression system used in this evaluation is the classical JPEG coder. Both objective and subjective tests were performed on a 50 natural image data base with a panel of experimented and non-experimented observers. The results show that these metrics are highly correlated with the subjective quality grading but also depends on the complexity of the images under study. They can be improved by integrating all masking effects and typical artifacts of the compression method. Their combination as a multidimensional quality measure in which each dimension is related to a properly identified artifact, should be based on a subjective evaluation. Most studies in the literature for video quality assessment have been focused on the evaluation of quantized video sequences at fixed and high spatial and temporal resolutions. Only limited work has been reported for assessing video quality under different spatial and temporal resolutions. In this paper, we consider a wider scope of video quality assessment in the sense of considering multiple dimensions. In particular, we address the problem of evaluating perceptual visual quality of low bit-rate videos under different settings and requirements. Extensive subjective view tests for assessing the perceptual quality of low bit-rate videos have been conducted, which cover 150 test scenarios and include five distinctive dimensions: encoder type, video content, bit rate, frame size, and frame rate. Based on the obtained subjective testing results, we perform thorough statistical analysis to study the influence of different dimensions on the perceptual quality and some interesting observations are pointed out. We believe such a study brings new knowledge into the topic of cross-dimensional video quality assessment and it has immediate applications in perceptual video adaptation for scalable video over mobile networks. This paper begins by considering a number of important design questions for a large-scale, widely available, multimedia test collection intended to support long-term scientific evaluation and comparison of content-based video analysis and exploitation systems. While the collection presented here is not quite web-scale, it is to our knowledge the largest video collection created to date. It is therefore of use in expanding the scale of any evaluation of multimedia collections and systems. Such exploitation systems would include the kinds of functionality already explored within the annual TREC Video Retrieval Evaluation (TRECVid) benchmarking activity such as search, semantic concept detection, and automatic summarization. We then report on our progress in creating such a multimedia collection from publicly available Internet Archive videos with Creative Commons licenses (IACC.1), which we hope will be a useful approximation of a web-scale collection and will support a next generation of benchmarking activities for content-based video operations. We also report on some possibilities for putting this collection to use in multimedia system evaluation. It is the intended that this collection be partitioned and used within the TRECVid 2010 evaluations, and in subsequent years to that. Objective image video quality metrics which accurately represent the subjective quality of processed images are of paramount importance for the design and assessment of an image compression and transmission system. In some scenarios, it is also important to evaluate the quality of the received image with minimal reference to the transmitted one. For instance, for closed-loop optimization of a transmission system, the image quality measure can be evaluated at the receiver and provided as feedback information to the system controller. The original image - prior to compression and transmission - is not usually available at the receiver side, and it is important to rely at the receiver side on an objective quality metric that does not need reference or needs minimal reference to the original image. The observation that the human eye is very sensitive to edge and contour information of an image underpins the proposal of our reduced reference (RR) quality metric, which compares edge information between the distorted and the original image. Results highlight that the metric correlates well with subjective observations, also in comparison with commonly used full-reference metrics and with a state-of-the-art reduced reference metric. Evaluation of coding efficiency is traditionally modeled as a continuous rate-distortion (R-D) function, where the peak signal-to-noise ratio (PSNR) is adopted as the quality measure. Although the PSNR-versus-bitrate curve offers some useful tradeoff information between video quality and coding bit-rates, it does not take human perceptual experience into account. In this work, by following the recent image video quality assessment framework based on the just-noticeable-difference (JND) notion, we conduct a subjective test for HEVC (High Efficiency Video Codec) video to measure the QP value that lies in the boundary of perceptually lossless and lossy coded bit streams for each human subject. This is also known as the first JND point. It is observed that the statistics of the first JND points of 30 subjects follows the normal distribution for a great majority of test sequences. Finally, a machine-learning approach is proposed to predict the mean of the group-based JND distribution based on extracted video features. It is shown by experimental results that the mean JND point can be predicted accurately. This work provides a MATLAB-based framework for image and video quality evaluation that integrates test content generation, subjective testing interfaces, objective quality metric evaluation and correlation analysis into a single application. It also provides an HTML-based help system and a Graphical User Interface that makes applications based on the framework user friendly. The framework supports a configurable interface that makes it easy to integrate existing or new quality metrics and to compare them against each other. The framework abstracts the user from underlying details like file format, report generation and test flow control. Wired and wireless data networks have witnessed a rapid proliferation of multimedia applications such as Internet video streaming, video conferencing, etc. A desirable key feature for multimedia transmission over multiuser environments is the ability of adapting rate and quality of video stream to different QoS conditions. The most efficient approach to address the scalability of multimedia applications is to encode video streams in compliance with the scalable video coding (SVC) standard, which is proposed as an extension to H.264 AVC standard. This paper addresses the rate control and bandwidth sharing for multimedia applications that are relying on SVC-encoded video streams. In previous studies, idealistic utility functions, mainly in the form of a staircase function, were used to cast the rate allocation for SVC-encoded streams as an optimization problem. These utility functions make the optimization problem non-convex and even non-differentiable, for which achieving optimality through dual-based approaches proves quite challenging. Towards this goal, we introduce an accurate analytical model of the utility function for SVC-encoded video streams. Using the abovementioned utility model and adopting the utility-proportional optimization approach, we come up with a convex formulation and propose a dual-based distributed algorithm for rate allocation of SVC-encoded streams. To the best of our knowledge, this is the first work that focuses on an accurate utility function modeling for SVC-encoded streams. Simulation experiments show that the proposed algorithm is quite efficient in achieving the convergence towards the global optimality. Simple quality metrics such as PSNR are known to not correlate well with subjective quality when tested across a wide spectrum of video content or quality regime. Recently, efforts have been made in designing objective quality metrics trained on subjective data, demonstrating better correlation with video quality perceived by human. Clearly, the accuracy of such a metric heavily depends on the quality of the subjective data that it is trained on. In this paper, we propose a new approach to recover subjective quality scores from noisy raw measurements, by jointly estimating the subjective quality of impaired videos, the bias and consistency of test subjects, and the ambiguity of video contents all together. Compared to previous methods which partially exploit the subjective information, our approach is able to exploit the information in full, yielding better handling of outliers without the need for z-scoring or subject rejection. It also handles missing data more gracefully. Lastly, as side information, it provides interesting insights on the test subjects and video contents. A continuous evaluation of the end user’s quality-of-experience (QoE) is essential for efficient video streaming. This is crucial for networks with constrained resources that offer time-varying channel quality to its users. In hypertext transfer protocol-based video streaming, the QoE is measured by quantifying the perceptual impact of distortions caused by rate adaptation or interruptions in playback due to rebuffering events. The resulting impact on the QoE due to these distortions has been studied individually in the literature. However, the QoE is determined by an interplay of these distortions, and therefore necessitates a combined study of them. To the best of our knowledge, there is no publicly available database that studies these distortions jointly on a continuous time basis. In this paper, our contributions are twofold. First, we present a database consisting of videos at full high definition and ultrahigh definition resolutions. We consider various levels of rate adaptation and rebuffering distortions together in these videos as experienced in a typical realistic setting. A subjective evaluation of these videos is conducted on a continuous time scale. Second, we present a QoE evaluation framework comprising a learning-based model during playback and an exponential model during rebuffering. Furthermore, we perform an objective evaluation of popular video quality assessment and continuous time QoE metrics over the constructed database. The objective evaluation study demonstrates that the performance of the proposed QoE model is superior to that of the objective metrics. The database is publicly available for download at http: www.iith.ac.in lfovia downloads.html . In this paper, we study the performance of a combined link- and physical-layer approach for the downlink of a dynamic OFDM-frequency division multiple access transmission system delivering MPEG-4 video streams. The approach consists of three different mechanisms-queue management, resource allocation, and subcarrier assignment-which can be used separately as well as in combination. This cross-layer approach allows to utilize channel-related knowledge and semantic information of the streams' packets. Consecutively adding these mechanisms to the studied system, we judge their respective impact on system performance. To do so, we present a new metric for quality assessment of long video transmissions (considerably longer than 10 s) and apply it to the simulation results. In particular, we introduce a local distortion threshold-based metric instead of an overall average judgment. We find that our combined system approach increases the number of supportable terminals per cell by up to 300 . We derive an optimization framework for joint view and rate scalable coding of multi-view video content represented in the texture plus depth format. The optimization enables the sender to select the subset of coded views and their encoding rates such that the aggregate distortion over a continuum of synthesized views is minimized. We construct the view and rate embedded bitstream such that it delivers optimal performance simultaneously over a discrete set of transmission rates. In conjunction, we develop a user interaction model that characterizes the view selection actions of the client as a Markov chain over a discrete state-space. We exploit the model within the context of our optimization to compute user-action-driven coding strategies that aim at enhancing the client's performance in terms of latency and video quality. Our optimization outperforms the state-of-the-art H.264 SVC codec as well as a multi-view wavelet-based coder equipped with a uniform rate allocation strategy, across all scenarios studied in our experiments. Equally important, we can achieve an arbitrarily fine granularity of encoding bit rates, while providing a novel functionality of view embedded encoding, unlike the other encoding methods that we examined. Finally, we observe that the interactivity-aware coding delivers superior performance over conventional allocation techniques that do not anticipate the client's view selection actions in their operation. Every user of multimedia technology expects good image and video visual quality independently of the particular characteristics of the receiver or the communication networks employed. Unfortunately, due to factors like processing power limitations and channel capabilities, images or video sequences are often downsampled and or transmitted or stored at low bitrates, resulting in a degradation of their final visual quality. In this paper, we propose a region-based framework for intentionally introducing downsampling of the high resolution (HR) image sequences before compression and then utilizing super resolution (SR) techniques for generating an HR video sequence at the decoder. Segmentation is performed at the encoder on groups of images to classify their blocks into three different types according to their motion and texture. The obtained segmentation is used to define the downsampling process at the encoder and it is encoded and provided to the decoder as side information in order to guide the SR process. All the components of the proposed framework are analyzed in detail. A particular implementation of it is described and tested experimentally. The experimental results validate the usefulness of the proposed method. With EvalVid we present a complete framework and tool-set for evaluation of the quality of video transmitted over a real or simulated communication network. Besides measuring QoS parameters of the underlying network, like loss rates, delays, and jitter, we support also a subjective video quality evaluation of the received video based on the frame-by-frame PSNR calculation. The tool-set has a modular construction, making it possible to exchange both the network and the codec. We present here its application for MPEG-4 as example. EvalVid is targeted for researchers who want to evaluate their network designs or setups in terms of user perceived video quality. The tool-set is publicly available [11]. Abstract A new methodology to measure coded image video quality using the just-noticeable-difference (JND) idea was proposed in (2015). Several small JND-based image video quality datasets were released by the Media Communications Lab at the University of Southern California in (2016) and (2016) [3]. In this work, we present an effort to build a large-scale JND-based coded video quality dataset. The dataset consists of 220 5-s sequences in four resolutions (i.e., 1920 × 1080 , 1280 × 720 , 960 × 540 and 640 × 360 ). For each of the 880 video clips, we encode it using the H.264 AVC codec with QP = 1 , … , 51 and measure the first three JND points with 30 + subjects. The dataset is called the “VideoSet”, which is an acronym for “Video Subject Evaluation Test (SET)”. This work describes the subjective test procedure, detection and removal of outlying measured data, and the properties of collected JND data. Finally, the significance and implications of the VideoSet to future video coding research and standardization efforts are pointed out. All source coded video clips as well as measured JND data included in the VideoSet are available to the public in the IEEE DataPort (, 2016 [4]).