text: 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.
  @cite_15: 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 .
  @cite_16: 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 .
  @cite_13: 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].
summary: On the other hand, it is also remarkable the seminal work (EvalVid) proposed by Klaue @cite_13 , a widely used tool-set for video transmission evaluation. This tool-set does actually include transmission, some quality of service (QoS) parameter estimation (packet loss, delay and jitter) and some bitstream parameter evaluations (frame loss rate and frame jitter). EvalVid video quality evaluation techniques include PSNR, Structural Similarity index (SSIM) @cite_16 , Mean Opinion Score (MOS, mapped from PSNR) and Distortion in Inverval (DIV, mapped from MOS as described in @cite_15 ).