Perceptual visual quality metrics: A survey

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.

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Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos

We propose a blind (no reference or NR) video quality evaluation model that is nondistortion specific. The approach relies on a spatio-temporal model of video scenes in the discrete cosine transform domain, and on a model that characterizes the type of motion occurring in the scenes, to predict video quality. We use the models to define video statistics and perceptual features that are the basis of a video quality assessment (VQA) algorithm that does not require the presence of a pristine video to compare against in order to predict a perceptual quality score. The contributions of this paper are threefold. 1) We propose a spatio-temporal natural scene statistics (NSS) model for videos. 2) We propose a motion model that quantifies motion coherency in video scenes. 3) We show that the proposed NSS and motion coherency models are appropriate for quality assessment of videos, and we utilize them to design a blind VQA algorithm that correlates highly with human judgments of quality. The proposed algorithm, called video BLIINDS, is tested on the LIVE VQA database and on the EPFL-PoliMi video database and shown to perform close to the level of top performing reduced and full reference VQA algorithms.

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Blind Prediction of Natural Video Quality

We present a family of reduced reference video quality assessment (QA) models that utilize spatial and temporal entropic differences. We adopt a hybrid approach of combining statistical models and perceptual principles to design QA algorithms. A Gaussian scale mixture model for the wavelet coefficients of frames and frame differences is used to measure the amount of spatial and temporal information differences between the reference and distorted videos, respectively. The spatial and temporal information differences are combined to obtain the spatio-temporal-reduced reference entropic differences. The algorithms are flexible in terms of the amount of side information required from the reference that can range between a single scalar per frame and the entire reference information. The spatio-temporal entropic differences are shown to correlate quite well with human judgments of quality, as demonstrated by experiments on the LIVE video quality assessment database.

Video Quality Assessment by Reduced Reference Spatio-Temporal Entropic Differencing

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Models in Biology: Mathematics, Statistics and Computing

The measurement of video quality for lossy and low-bitrate network transmissions is a challenging topic. Especially, the temporal artifacts which are introduced by video transmission systems and their effects on the viewer's satisfaction have to be addressed. This paper focuses on a framework that adds a temporal distortion awareness to typical video quality measurement algorithms. A motion estimation is used to track image areas over time. Based on the motion vectors and the motion prediction error, the appearance of new image areas and the display time of objects is evaluated. Additionally, degradations which stick to moving objects can be judged more exactly. An implementation of this framework for multimedia sequences, e.g., QCIF, CIF, or VGA resolution, is presented in detail. It shows that the processing steps and the signal representations that are generated by the algorithm follow the reasoning of a human observer in a subjective experiment. The improvements that can be achieved with the newly proposed algorithm are demonstrated using the results of the Multimedia Phase I database of the Video Quality Experts Group.

Temporal Trajectory Aware Video Quality Measure

Recent developments have given birth to H.264/AVC: a video coding standard offering better bandwidth to video quality ratios than previous standards (such as H.263, MPEG-2, MPEG-4, etc.), due to its improved inter- and intraprediction modes at the expense of higher computation complexity. It is expected that H.264/AVC will take over the digital video market, replacing the use of previous standards in most digital video applications. This creates an important need for heterogeneous video transcoding technologies from older standards to H.264. In this paper, we focus our attention on the interframe prediction, the most computationally intensive task involved in the heterogeneous video transcoding process. This paper presents a novel macroblock (MB) mode decision algorithm for interframe prediction based on data mining techniques to be used as part of a very low complexity heterogeneous video transcoder. The proposed approach is based on the hypothesis that MB coding mode decisions in H.264 video have a correlation with the distribution of the motion compensated residual in the decoded video. We use data mining tools to exploit the correlation and derive decision trees to classify the incoming decoded MBs into one of the several coding modes in H.264. The proposed approach reduces the H.264 MB mode computation process into a decision tree lookup with very low complexity. For general validation purposes, we apply our algorithm to two of the most important heterogeneous video transcoders: MPEG-2 to H.264 and H.263 to H.264. Our results show that the our data-mining based transcoding algorithm is able to maintain a good video quality while considerably reducing the computational complexity by 72% on average when applied in MPEG-2 to H.264 transcoders, and by 62% on average when applied in H.263 to H.264 transcoders. Finally, we conduct a comparative study with some of the most prominent fast interprediction methods for H.264 presented in the literature. Our results show that the proposed data mining-based approach achieves the best results for video transcoding applications.

Low-Complexity Heterogeneous Video Transcoding Using Data Mining

In the evaluation of video quality often a full reference approach is used, thus calculating some measure of difference between the reference frames and the distorted frames. Often this measure returns one value per pixel, in the simplest case the squared difference. Conventionally, this pixel based measure is averaged over space and time. This paper introduces a psychophysically derived algorithm for this step. It uses the distribution of the cells in the fovea and the assumption that in a subjective test the part with the highest distortion is most important. Additionally, a temporal integration step is proposed which models the recency and forgiveness effect. Different video quality measures are enhanced with these two steps and their performance is evaluated using the results of a subjective test.

Perceptually motivated spatial and temporal integration of pixel based video quality measures

In the past 10 years detailed works on different video transcoders have been published. However, the new ITU-T Recommendation H.264--also adapted as ISO/IEC MPEG-4 Part 10 (AVC)--provides many new encoding options for the prediction processes that lead to difficulties for low complexity transcoding. In this work we present very fast transcoding techniques to convert H.263 bitstreams into H.264/AVC bitstreams. We will give reasoning, why the proposed pixel domain approach is advantageous in this scenario instead of using a DCT domain transcoder. Our approach results in less than 9% higher data rate at equivalent PSNR quality compared to a full-search approach. But this rate loss allows the reduction of the search complexity by a factor of over 200 for inter frames and still a reduction of over 70% for intra frames. A comparison to a fast search algorithm is given. We also provide simulation results that our algorithm works for transcoding MPEG-2 to H.264/AVC in the aimed scenario.

Fast video transcoding from H.263 to H.264/MPEG-4 AVC

The quality of coded images is often assessed by a subjective test. Usually the viewers get as much time as they need to find a stable result. In video sequences however, the viewer has to judge the quality in a shorter time that is defined by the changing content or a following scene cut. Therefore it is desirable to know the influence of a shorter presentation time on the perceptibility of distortions. In this paper we present the results of a suitable subjective test on coded still images. The images were presented for six different durations, ranging from 200 ms to 3 s. Special care was taken to avoid the memorization effect usually present after short presentations. The results show that the viewers tend to avoid extreme votings at short durations. The variance of the votings is also discussed in detail. Based on the result of the voting for the longest presentation time, we propose a prediction model for the voting of the shorter durations using a logistic curve fit. This presentation time model (PTM) is presented and analysed in detail.

Jens Bialkowski

Papers

Temporal Trajectory Aware Video Quality Measure

Low-Complexity Heterogeneous Video Transcoding Using Data Mining

Perceptually motivated spatial and temporal integration of pixel based video quality measures

Fast video transcoding from H.263 to H.264/MPEG-4 AVC

Influence of the Presentation Time on Subjective Votings of Coded Still Images