Real-Time Reference-Free Video Quality Measurement for Multimedia Communication

Objective video quality measurements emerge as an important issue as multimedia data is increasingly transmitted over the channels where bandwidth may not be guaranteed. Among various objective models for video quality measurement, no-reference models have the largest application areas. In this paper, we propose a no-reference video quality assessment method for H.264 using artificial neural networks. Various features are extracted from H.264 bit-stream data and these features are inputted to a neural network. The neural network is trained to predict subjective video quality scores obtained by a number of evaluators. Experimental results show promising results, though a larger database would be required to train neural networks to provide robust performance.

This paper presents an objective video quality evaluation method for quantifying the subjective quality of digital mobile video. The proposed method aims to objectify the subjective quality by extracting edgeness and blockiness parameters. To evaluate the performance of the proposed algorithms, we carried out subjective video quality tests with the double-stimulus continuous quality scale method and obtained differential mean opinion score values for 120 mobile video clips. We then compared the performance of the proposed methods with that of existing methods in terms of the differential mean opinion score with 120 mobile video clips. Experimental results showed that the proposed methods were approximately 10% better than the edge peak signal-to-noise ratio of the J.247 method in terms of the Pearson correlation.

The purpose of this study was to examine the impact of the desirability of content on viewers' ratings of subjective video quality. Most subjective video quality studies use short-duration clips that are specially designed to exercise the encoding algorithms and do not consider the desirability of the content as a variable. In four studies, we employed a total of 100 participants and 180 movie clips encoded at nine levels from 550 kbps up to DVD quality. Participants viewed clips that were 2 min in length and then were asked about video quality of the clips and desirability of the movie content. The results of these studies show that there is a strong correlation between the desirability of movie content and subjective ratings of video quality. This strong relationship holds across a wide range of encoding levels and movie content when that content is viewed under longer, more naturalistic viewing conditions. The effects of content should be considered when evaluating the subjective quality of encoded video content, as these effects can be as large as those seen between low- and high-quality encodings. Researchers and practitioners trying to determine acceptable levels of video quality for actual consumption by consumers may find that the results and methods described here allow for a more accurate assessment of levels of video quality that are acceptable in a fielded service.

In heterogeneous network environments, it is mandatory to measure the grade of the video quality in order to guarantee the optimal quality of the video streaming service. Quality of Service (QoS) has become a key issue for service acceptability and user satisfaction. Although there have been many recent works regarding video quality, most of them have been limited to measuring quality within temporal and Signal-to-Noise Ratio (SNR) scalability. H.264/AVC Scalable Video Coding (SVC) has emerged and has been developed to support full scalability. This includes spatial, temporal, and SNR scalability, each of which shows different visual effects. The aim of this paper is to define and develop a novel video quality metric allowing full scalability. It focuses on the effect of frame rate, SNR, the change of spatial resolution, and motion characteristics using subjective quality assessment. Experimental results show the proposed quality metric has a high correlation to subjective quality and that it is useful in determining the video quality of SVC.

Subjektives Qualitätsverständnis von Vertragszahnärzten im Umgang mit Qualitätsmanagement – Ergebnisse eines bundesweiten Surveys

Today in many homes big TV screens and hifi systems are common. But is the perception of subjective video quality under professional test conditions the same as at home? Therefore we examined two things: How large is the influence of the presentation device but also the influence of the soundtrack, both in HDTV (1080p50). Previous work has shown that a difference is noticeable, but there have not been studies with consumer devices, yet. It was also shown that there is an influence of the soundtrack, but only in SDTV or lower resolutions. Therefore we conducted subjective video tests: One test with different devices, a 23-inch-reference monitor, a high quality 56-inch-LCD-TV and an HD-projector, and one test in which we presented a soundtrack on a 7.1-channel hifi system in addition to the HD-projector. The results show two things: First the test subjects had a higher quality of experience with the consumer devices than with the reference monitor, although the video quality of the reference monitor itself was rated better in an additional questionnaire and the mean opinion score (MOS). The second result is that there is no significant difference in the MOS between showing the videos on the projector with or without sound.

With the exponential growth of video traffic over wireless networked and embedded devices, mechanisms are needed to predict and control perceptual video quality to meet the quality of experience (QoE) requirements in an energy-efficient way. This paper proposes an energy-efficient QoE support framework for wireless video communications. It consists of two components: 1) a perceptual video quality model that allows the prediction of video quality in real-time and with low complexity, and 2) an application layer energy-efficient and content-aware forward error correction (FEC) scheme for preventing quality degradation caused by network packet losses. The perceptual video quality model characterizes factors related to video content as well as distortion caused by compression and transmission. Prediction of perceptual quality is achieved through a decision tree using a set of observable features from the compressed bitstream and the network. The proposed model can achieve prediction accuracy of 88.9% and 90.5% on two distinct testing sets. Based on the proposed quality model, a novel FEC scheme is introduced to protect video packets from losses during transmission. Given a user-defined perceptual quality requirement, the FEC scheme adjusts the level of protection for different components in a video stream to minimize network overhead. Simulation results show that the proposed FEC scheme can enhance the perceptual quality of videos. Compared to conventional FEC methods for video communications, the proposed FEC scheme can reduce network overhead by 41% on average.

In this paper some possibilities of using the GPGPU programming techniques for a fast estimation of the recently proposed combined video quality metric are discussed. Such metric consists of three state-of-the-art image quality assessment metrics applied using frame-by-frame analysis with appropriate weighting coefficients. Since this combined metric is better correlated with subjective quality scores than each of its components, especially for the contaminations typical for the wireless transmission of compressed video data, the next step is related to its efficient implementation useful for real-time applications. In the paper an efficient implementation of the estimated combined metric is presented together with the verification of its linear correlation with subjective video quality evaluations performed using the LIVE Wireless Video Quality Assessment Database containing 160 video files with four types of distortions and their Differential Mean Opinion Score (DMOS) values.

Most traditional video communication systems consider videos as low-level bit streams, ignoring the underlying visual content. Content-aware video communication is a new framework that explores the strong correlation between video content, resource (bit rate), and utility (quality). Such a framework facilitates new ways of quality modeling and resource allocation in multimedia communication. We demonstrate advantages of the content-aware approaches in two applications. First, content-aware models were developed for predicting video traffic for live video streams. The video traffic models were evaluated in a dynamic network resource allocation system. Our simulations have shown that, compared to existing techniques, significant reduction (55% to 70%) in required network resources can be achieved. Second, we have used the content-aware principle for automatic generation of utility function (subjective quality vs. bit rate) for live video. Our results indicate that high accuracy in estimating utility functions can be achieved. Such utility functions can be applied to optimal transcoding and media scaling in distributed network environments.

Objective video quality measurement is a challenging problem in a variety of video processing application ranging from lossy compression to printing. An ideal video quality measure should be able to mimic the human observer. We present a new video quality measure, M-SVD, to evaluate distorted video sequences based on singular value decomposition. A computationally efficient approach is developed for full-reference (FR) video quality assessment. This measure is tested on the Video Quality Experts Group (VQEG) phase I FR-TV test data set. Our experiments show the graphical measure displays the amount of distortion as well as the distribution of error in all frames of the video sequence while the numerical measure has a good correlation with perceived video quality outperforms PSNR and other objective measures by a clear margin.

Since different regions in picture have different sorts of texture, non-uniform quantization should be considered in macroblock level to meet the picture content. A parameterized frequency weighting and non-uniform macroblock level weighting scheme is proposed to improve the subjective quality of compressed video. This scheme is targeted to preserve texture details as well as region edge of video content in quantization process, which brings better subjective performance for the high definition video content coding

The consumption of digital videos increases every year. In addition to the fact that many countries already use digital TV, currently the traffic of internet video services are more than 60% of the total internet traffic. The growth of digital video consumption demands a viable method to measure the video quality. Objective video quality assessment methods are algorithms that estimates video quality. Recent quality assessment methods provide quality predictions that are well correlated with the subjective quality scores. However, most of these methods are very complex and takes long periods to compute.

Well-performed Video quality assessment (VQA) method should be consistent with human visual systems for better prediction accuracy. In this paper, we propose a VQA method using motion-compensated temporal filtering (MCTF) and manifold feature similarity. To be more specific, a group of frames (GoF) is first decomposed into a temporal high-pass component (HPC) and a temporal low-pass component (LPC) by MCTF. Following this, manifold feature learning (MFL) and phase congruency (PC) are used to predict the quality of temporal LPC and temporal HPC respectively. The quality measures of the LPC and the HPC are then combined as GoF quality. A temporal pooling strategy is subsequently used to integrate GoF qualities into an overall video quality. The proposed VQA method appropriately processes temporal information in video by MCTF and temporal pooling strategy, and simulate human visual perception by MFL. Experiments on publicly available video quality database showed that in comparison with several state-of-the-art VQA methods, the proposed VQA method achieves better consistency with subjective video quality and can predict video quality more accurately.

A tremendous number of objective video quality measurement algorithms have been developed during the last two decades. Most of them either measure a very limited aspect of the perceived video quality or they measure broad ranges of quality with limited prediction accuracy. This paper lists several perceptual artifacts that may be computationally measured in an isolated algorithm and some of the modeling approaches that have been proposed to predict the resulting quality from those algorithms. These algorithms usually have a very limited application scope but have been verified carefully. The paper continues with a review of some standardized and well-known video quality measurement algorithms that are meant for a wide range of applications, thus have a larger scope. Their individual artifacts prediction accuracy is usually lower but some of them were validated to perform sufficiently well for standardization. Several difficulties and shortcomings in developing a general purpose model with high prediction performance are identified such as a common objective quality scale or the behavior of individual indicators when confronted with stimuli that are out of their prediction scope. The paper concludes with a systematic framework approach to tackle the development of a hybrid video quality measurement in a joint research collaboration.

In wireless networks, the value of cooperative communication cannot be over stated. It has attracted a lot of attention because of its capacity to successfully address the problem of fading in wireless communication. With the use of relays between source and destination, the quality of information received at the destination improves dramatically. The problem addressed here is that when video is sent straight from source to destination on an ad hoc network, the quality is poor. Video quality can be improved by adding a relay node between the source and destination, which uses a decode and forward approach before sending the data to the destination. To combine the signal received from the source and the relay, maximum ratio combining (MRC) is used at the destination. This is the concept of cooperative communication. In the presence of additive white Gaussian noise (AWGN), video is conveyed using BPSK/QPSK modulation technique over a Rayleigh fading channel in 802.11b wireless ad hoc network. The video quality is analyzed in terms of Peak signal to noise ratio, latency and bit error rate in the presence of convolutional coding as well as in the absence of convolutional coding. The results indicated that measured video quality with relay was considerably better than the measured video quality without relay, emphasizing the necessity of cooperative communication.