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
2069094335
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. 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. Low latency audio coding gains increasing importance among upcoming high quality communication applications like video conferencing and VoIP. This paper provides a comparison of two low latency audio codecs suitable for these tasks: MPEG-4 ER AAC-LD and ITU-T G.722.1 Annex C. Despite their similar coding strategies, both codecs show significant differences with respect to used tools and coding performance. A comparison of the coding tools is provided and the influence on different signal classes is discussed. 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. Recent advances in technology have made tremendous amounts of multimedia information available to the general population. An efficient way of dealing with this new development is to develop browsing tools that distill multimedia data as information oriented summaries. Such an approach will not only suit resource poor environments such as wireless and mobile, but also enhance browsing on the wired side for applications like digital libraries and repositories. Automatic summarization and indexing techniques will give users an opportunity to browse and select multimedia document of their choice for complete viewing later. In this paper, we present a technique by which we can automatically gather the frames of interest in a video for purposes of summarization. Our proposed technique is based on using Delaunay Triangulation for clustering the frames in videos. We represent the frame contents as multi-dimensional point data and use Delaunay Triangulation for clustering them. We propose a novel video summarization technique by using Delaunay clusters that generates good quality summaries with fewer frames and less redundancy when compared to other schemes. In contrast to many of the other clustering techniques, the Delaunay clustering algorithm is fully automatic with no user specified parameters and is well suited for batch processing. We demonstrate these and other desirable properties of the proposed algorithm by testing it on a collection of videos from Open Video Project. We provide a meaningful comparison between results of the proposed summarization technique with Open Video storyboard and K-means clustering. We evaluate the results in terms of metrics that measure the content representational value of the proposed technique. In this paper we propose a new method for Codec Adaptation Over Software-defined networks: CAoS. This is based on a software-defined network architecture and implements an efficient and innovator approach for voice degradation avoidance in enterprise networks. Enterprise scarce WAN links queue voice traffic and also limit it to a certain number of calls by performing QoS and CAC on border elements. This study proposes the use of an existing centralized SDN controller to monitor real-time voice traffic and to avoid voice congestion. We implement a codec adaptation to VoIP peers that are competing for scarce throughput. Differently from other approaches, a SDN-based network already counts on a centralized controller, which can perform other type of features, like CAoS, without adding any other hardware to the network, optimizing company CAPEX. Emulation tests were performed using Mininet, POX controller and programmable VoIP clients. CAoS module was added to the software-based controller and it was divided in two sub-modules: monitoring and decision modules. Programmable VoIP clients received a third module, called SIP module, which is responsible for effectively updating the codec in use. The results show that quality of voice increases significantly when congestion occurs in a link and CAoS is running. Forward error correction (FEC) schemes have been proposed and used successfully for multicasting realtime video content to groups of users. Under traditional IP multicast, application-level FEC can only be implemented on an end-to-end basis between the sender and the clients. Emerging overlay and peer-to-peer (p2p) networks open the door for new paradigms of network FEC. The deployment of FEC within these emerging networks has received very little attention (if any). In this paper, we analyze and optimize the impact of network-embedded FEC (NEF) in overlay and p2p multimedia multicast networks. Under NEF, we place FEC codecs in selected intermediate nodes of a multicast tree. The NEF codecs detect and recover lost packets within FEC blocks at earlier stages before these blocks arrive at deeper intermediate nodes or at the final leaf nodes. This approach significantly reduces the probability of receiving undecodable FEC blocks. In essence, the proposed NEF codecs work as signal regenerators in a communication system and can reconstruct most of the lost data packets without requiring retransmission. We develop an optimization algorithm for the placement of NEF codecs within random multicast trees. Based on extensive H.264 video simulations, we show that this approach provides significant improvements in video quality, both visually and in terms of PSNR values. With the 802.11 WLAN multimedia applications (Video, Audio, real-time voice over IP,…) increasing, providing Quality of Service (QoS) support becomes very important since the original standard doesn’t take QoS into account. The standard offers access to the wireless users only regarding physical considerations. This can lead to overloaded access points (AP) and considerable degradation of the QoS. This paper deals with this problem. It focuses on the presentation of a QoS management solution for wireless communication systems. It mainly defends that a balanced distribution of mobile stations among the available access points leads to better performances of the Wireless LAN. Some OPNET simulations of the proposed approach are presented to show a better resources allocation and efficiency on QoS metrics. A protocol structure between mobiles and APs is also specified for the implementation of this approach. An SDL description and MSC simulation of this protocol is provided as a first step in its development. In recent years, Wireless Local Area Networks (WLAN) have become the premier choice for many homes and enterprises. WiMAX (Worldwide Interoperability for Microwave Access) has also emerged as the wireless standard that aims to deliver data over long distances, and can potentially provide wireless broadband access as an alternative to the wired cable and DSL networks. Parallel with the surge of wireless networks is the explosive growth of multimedia applications. Therefore, it is important to explore efficient methods for delivering multimedia data in such wireless settings. In this paper, we propose a network coding based scheduling policy to be used at WLAN-like Access Point (AP) or at a WiMAX-like broadcast station that optimizes the multimedia transmission in both broadcast and unicast settings. In particular, the contributions of this paper include (a) a framework for increasing the bandwidth efficiency of broadcast and unicast sessions in a wireless network based on network coding techniques and (b) an optimized scheduling algorithm based on the Markov Decision Process (MDP) to maximize the quality of multimedia applications. Simulations and theoretical results demonstrate the advantages of our approach over the conventional techniques. 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. 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. Experimental data are presented that clearly demonstrate the scope of application of peak signal-to-noise ratio (PSNR) as a video quality metric. It is shown that as long as the video content and the codec type are not changed, PSNR is a valid quality measure. However, when the content is changed, correlation between subjective quality and PSNR is highly reduced. Hence PSNR cannot be a reliable method for assessing the video quality across different video contents. Many companies struggle with unexpectedly high maintenance costs for their software development which are often caused by insufficient code quality. Although companies often use static analyses tools, they do not derive consequences from the metric results and, hence, the code quality does not actually improve. We provide an experience report of the quality consulting company CQSE, and show how code quality can be improved in practice: we revise our former expectations on quality control from [1] and propose an enhanced continuous quality control process which requires the combination of metrics, manual action, and a close cooperation between quality engineers, developers, and managers. We show the applicability of our approach with a case study on 41 systems of Munich RE and demonstrate its impact. We address the problem of simplifying Portuguese texts at the sentence level by treating it as a “translation task”. We use the Statistical Machine Translation (SMT) framework to learn how to translate from complex to simplified sentences. Given a parallel corpus of original and simplified texts, aligned at the sentence level, we train a standard SMT system and evaluate the “translations” produced using both standard SMT metrics like BLEU and manual inspection. Results are promising according to both evaluations, showing that while the model is usually overcautious in producing simplifications, the overall quality of the sentences is not degraded and certain types of simplification operations, mainly lexical, are appropriately captured. Convolutional neural networks (CNNs) have been widely used in image processing community. Image deblocking is a post-processing strategy, which aims to reduce the visually annoying blocking artifacts that are caused by block-based transform coding at low bit rates. In recent years, CNNs based methods have been proposed to solve this classic image processing problem. In this paper, we present an efficient deep C-NNs model for image deblocking. Our model can well alleviate the conflict between bit reduction and quality preservation by taking local small patches into consideration. Our trained model can be used to deblock lossy compressed images with different quality factors. The proposed model can be easily integrated into the existing codecs as a post-processing procedure without changing the codec. Experimental results verify that our proposed model outperforms the state-of-the-art methods in both the objective quality and the perceptual quality. Abstract The huge amount of degraded documents stored in libraries and archives around the world needs automatic procedures of enhancement, classification, transliteration, etc. While high-quality images of these documents are in general easy to be captured, the amount of damage these documents contain before imaging is unknown. It is highly desirable to measure the severity of degradation that each document image contains. The degradation assessment can be used in tuning parameters of processing algorithms, selecting the proper algorithm, finding damaged or exceptional documents, among other applications. In this paper, the first dataset of degraded document images along with the human opinion scores for each document image is introduced in order to evaluate the image quality assessment metrics on historical document images. In this research, human judgments on the overall quality of the document image are used instead of the previously used OCR performance. Also, we propose an objective no reference quality metric based on the statistics of the mean subtracted contrast normalized (MSCN) coefficients computed from segmented layers of each document image. The segmentation into four layers of foreground and background is done on the basis of an analysis of the log-Gabor filters. This segmentation is based on the assumption that the sensitivity of the human visual system (HVS) is different at the locations of text and non-text. Experimental results show that the proposed metric has comparable or better performance than the state-of-the-art metrics, while it has a moderate complexity. The developed dataset as well as the Matlab source code of the proposed metric is available at http: www.synchromedia.ca system files VDIQA.zip . Objective metrics are evaluated on quality assessment of multidimensional video scalability.Both generic image video metrics and scalability-targeting metrics are included.Evaluation is systematically performed on three different data sets.In-depth analysis in terms of performance and complexity of metrics is provided.Overall, scalability-targeting metrics show best performance. Multidimensional video scalability refers to the possibility that a video sequence can be adapted according to given conditions of video consumption by adjusting one or more of its features such as frame size, frame rate, and spatial quality. An important issue in implementing an adaptive video distribution scheme using scalability is how to maximize the quality of experience for the delivered contents, which raises a more fundamental issue, that is, how to estimate perceived quality of scalable video contents. This paper evaluates existing state-of-the-art objective quality metrics, including both generic image video metrics and ones particularly developed for scalable videos, on the problem of quality assessment of multidimensional video scalability. It is shown that, on the whole, some recently developed metrics targeting scalability perform best. The results are thoroughly discussed in relation to the nature of the problem in comparison to what has been reported in existing studies for other problems. Graph clustering, the process of discovering groups of similar vertices in a graph, is a very interesting area of study, with applications in many different scenarios. One of the most important aspects of graph clustering is the evaluation of cluster quality, which is important not only to measure the effectiveness of clustering algorithms, but also to give insights on the dynamics of relationships in a given network. Many quality evaluation metrics for graph clustering have been proposed in the literature, but there is no consensus on how do they compare to each other and how well they perform on different kinds of graphs. In this work we study five major graph clustering quality metrics in terms of their formal biases and their behavior when applied to clusters found by four implementations of classic graph clustering algorithms on five large, real world graphs. Our results show that those popular quality metrics have strong biases toward incorrectly awarding good scores to some kinds of clusters, especially seen in larger networks. They also indicate that currently used clustering algorithms and quality metrics do not behave as expected when cluster structures are different from the more traditional, clique-like ones. End-to-end packet delay is an important performance parameter on the Internet, because it heavily affects the quality of real-time applications. Currently, however, because the packet transmission quality (e.g., transmission delay, jitter, packet loss) may vary dynamically, it is not easy to handle real-time traffic. For UDP-based real-time applications, a smoothing buffer (playout buffer) is typically used at the client to compensate for variable delays. The issue of playout control has been studied previously, and several algorithms for controlling the playout buffer have been proposed. These studies considered the network parameters (e.g., packet loss ratio and playout delay), but not the quality perceived by end users. We first clarify the relations between mean opinion score (MOS) of played audio and the network parameters (e.g., packet loss, packet transmission delay, and transmission rate). Then, utilizing the MOS function, we propose a new playout buffer algorithm that considers the user's perceived quality for real-time applications. Our simulation and implementation tests show that the algorithm can enhance the perceived quality more effectively than existing algorithms. Omnidirectional video (cylindrical or spherical) is a new media becoming more and more popular thanks to its interactivity for online multimedia applications such as Google Street View as well as for video surveillance and robotics applications. Interactivity in this context means that the user is able to explore and navigate audio-visual scenes by freely choosing viewpoint and viewing direction. In order to provide this key feature, omnidirectional video is typically represented as a classical two-dimensional (2D) rectangular panorama video that is mapped onto a (spherical or cylindrical) mesh and then rendered on the client's screen. Early transmission models of this full panorama video and mesh content simply deal with the panorama as a high-resolution video to be encoded at uniform quality. Generally the user can only view a restricted field-of-view of the content and then interact with pan-tilt-zoom commands. This means that a significant part of the bandwidth is wasted by transmitting quality video in regions that are not being visualized. In this paper we evaluate the relevance and optimality of a personalized transmission where quality is modulated in spherical or cylindrical regions depending on their likelihood to be viewed during a live user interaction. We show, based on interaction delay as well as bandwidth constraints, how tiling and predictive methods can improve on existing methods. © 2012 Alcatel-Lucent.