This paper proposes a No-Reference (NR) Video Quality Assessment (VQA) method for videos subject to the distortion given by the High Efficiency Video Coding (HEVC) scheme. The assessment is performed without access to the bitstream. The proposed analysis is based on the transform coefficients estimated from the decoded video pixels, which is used to estimate the level of quantization. The information from this analysis is exploited to assess the video quality. HEVC transform coefficients are modeled with a joint-Cauchy probability density function in the proposed method. To generate VQA features the quantization step used in the Intra coding is estimated. We map the obtained HEVC features using an Elastic Net to predict subjective video quality scores, Mean Opinion Scores (MOS). The performance is verified on a dataset consisting of HEVC coded 4 K UHD (resolution equal to 3840 x 2160) video sequences at different bitrates and spanning a wide range of content. The results show that the quality scores computed by the proposed method are highly correlated with the mean subjective assessments. (C) 2017 Elsevier Inc. All rights reserved.

In this work we present several methods for fast integer motion estimation of videos recorded aboard an Unmanned Aerial Vehicle (UAV). Different from related work, the field depth is not considered to be consistent. The novel methods designed for low complexity MV prediction in H.264/AVC and analysis hereof include histogram-based prediction, constant global motion, and modification of the candidate sets in the Enhanced Predictive Zonal Search (EPZS). The results verify the applicability of the methods with a speed-up factor of integer motion estimation up to 2-4. Initial results for UAV infrared (IR) video are also provided.

Video streaming services are offered over the Internet and since the service providers do not have full control over the network conditions all the way to the end user, streaming technologies have been developed to maintain the quality of service in these varying network conditions i.e. so called adaptive video streaming. In order to cater for users' Quality of Experience (QoE) requirements, HTTP based adaptive streaming solutions of video services have become popular. However, the keys to ensure the users a good QoE with this technology is still not completely understood. User QoE feedback is therefore instrumental in improving this understanding. Controlled laboratory based perceptual quality experiments that involve a panel of human viewers are considered to be the most valid method of the assessment of QoE. Besides laboratory based subjective experiments, crowdsourcing based subjective assessment of video quality is gaining popularity as an alternative method. This article presents insights into a study that investigates perceptual preferences of various adaptive video streaming scenarios through crowdsourcing based and laboratory based subjective assessment. The major novel contribution of this study is the application of Paired Comparison based subjective assessment in a crowdsourcing environment. The obtained results provide some novel indications, besides confirming the earlier published trends, of perceptual preferences for adaptive scenarios of video streaming. Our study suggests that in a network environment with fluctuations in the bandwidth, a medium or low video bitrate which can be kept constant is the best approach. Moreover, if there are only a few drops in bandwidth, one can choose a medium or high bitrate with a single or few buffering events.

Objective video quality metrics are designed to estimate the quality of experience of the end user. However, these objective metrics are usually validated with video streams degraded under common distortion types. In the presented work, we analyze the performance of published and known full-reference and noreference quality metrics in estimating the perceived quality of adaptive bit-rate video streams knowingly out of scope. Experimental results indicate not surprisingly that state of the art objective quality metrics overlook the perceived degradations in the adaptive video streams and perform poorly in estimating the subjective quality results.

The HTTP Adaptive Streaming (HAS) technology allows video service providers to improve the network utilization and thereby increasing the end-users’ Quality of Experience (QoE). This has made HAS a widely used approach for audiovisual delivery. There are several previous studies aiming to identify the factors influencing on subjective QoE of adaptation events. However, adapting the video quality typically lasts in a time scale much longer than what current standardized subjective testing methods are designed for, thus making the full matrix design of the experiment on an event level hard to achieve. In this study, we investigated the overall subjective QoE of 6 minutes long video sequences containing different sequential adaptation events. This was compared to a dataset from our previous work performed to evaluate the individual adaptation events. We could then derive a relationship between the overall mean opinion score (MOS) and the MOS from shorter sequences. The aforementioned empirical dataset has proven to be very challenging in terms of video quality assessment test design, thus deriving a conclusive outcome about the influence of different parameters have been difficult. The second contribution of this study is considering how objective characterizations of adapted videos can improve the understanding of the subjective ratings.

This paper investigates the impact of ambient light and peak white (maximum brightness of a display) on the perceived quality of videos displayed using local backlight dimming. Two subjective tests providing quality evaluations are presented and analyzed. The analyses of variance show significant interactions of the factors peak white and ambient light with the perceived quality. Therefore, we proceed to predict the subjective quality grades with objective measures. The rendering of the frames on liquid crystal displays with light emitting diodes backlight at various ambient light and peak white levels is computed using a model of the display. Widely used objective quality metrics are applied based on the rendering models of the videos to predict the subjective evaluations. As these predictions are not satisfying, three machine learning methods are applied: partial least square regression, elastic net, and support vector regression. The elastic net method obtains the best prediction accuracy with a spearman rank order correlation coefficient of 0.71, and two features are identified as having a major influence on the visual quality.

This paper proposes a No-Reference (NR) Video Quality Assessment (VQA) method for videos subject to the distortion given by High Efficiency Video Coding (HEVC). The proposed assessment can be performed either as a Bitstream-Based (BB) method or as a Pixel-Based (PB). It extracts or estimates the transform coefficients, estimates the distortion, and assesses the video quality. The proposed scheme generates VQA features based on Intra coded frames, and then maps features using an Elastic Net to predict subjective video quality. A set of HEVC coded 4K UHD sequences are tested. Results show that the quality scores computed by the proposed method are highly correlated with the subjective assessment.

This paper analyzes the impact on compensating for Type-I errors in video quality assessment. A Type-I error is to incorrectly conclude that there is an effect. The risk increases with the number of comparisons that are performed in statistical tests. Type-I errors are an issue often neglected in Quality of Experience and video quality assessment analysis. Examples are given for the analysis of subjective experiments and the evaluation of objective metrics by correlation.

In this work we compare a simple and a complex Machine Learning (ML) method used for the purpose of Video Quality Assessment (VQA). The simple ML method chosen is the Elastic Net (EN), which is a regularized linear regression model and easier to interpret. The more complex method chosen is Support Vector Regression (SVR), which has gained popularity in VQA research. Additionally, we present an ML-based feature selection method. Also, it is investigated how well the methods perform when tested on videos from other datasets. Our results show that content-independent cross-validation performance on a single dataset can be misleading and that in the case of very limited training and test data, especially in regards to different content as is the case for many video datasets, a simple ML approach is the better choice.

Adaptive bitrate (ABR) streaming is widely used for distribution of videos over the internet. In this work, we investigate how well we can predict the quality of such videos using well-known image metrics, information about the bitrate levels, and a relatively simple machine learning method. Quality assessment of ABR videos is a hard problem, but our initial results are promising. We obtain a Spearman rank order correlation of 0.88 using content-independent cross-validation.

We present a method for No-Reference (NR) Video Quality Assessment (VQA) for decoded video without access to the bitstream. This is achieved by extracting and pooling features from a NR image quality assessment method used frame by frame. We also present methods to identify the video coding and estimate the video coding parameters for MPEG-2 and H.264/AVC which can be used to improve the VQA. The analysis differs from most other video coding analysis methods since it is without access to the bitstream. The results show that our proposed method is competitive with other recent NR VQA methods for MPEG-2 and H.264/AVC.

This paper considers the problem of maximizing the entropy of two-dimensional (2D) Pickard Random Fields (PRF) subject to constraints. We consider binary Pickard Random Fields, which provides a 2D causal finite context model and use it to define stationary probabilities for 2×2 squares, thus allowing us to calculate the entropy of the field. All possible binary 2×2 constraints are considered and all constraints are categorized into groups according to their properties. For constraints which can be modeled by a PRF approach and with positive entropy, we characterize and provide statistics of the maximum PRF entropy. As examples, we consider the well known hard square constraint along with a few other constraints.

In order to cater for user's quality of experience (QoE) requirements, HTTP adaptive streaming (HAS) based solutions of video services have become popular recently. User QoE feedback can be instrumental in improving the capabilities of such services. Perceptual quality experiments that involve humans are considered to be the most valid method of the assessment of QoE. Besides lab-based subjective experiments, crowdsourcing based subjective assessment of video quality is gaining popularity as an alternative method. This paper presents insights into a study that investigates perceptual preferences of various adaptive video streaming scenarios through crowdsourcing based subjective quality assessment.

We consider Distributed Video Coding (DVC) in presence of communication errors. First, we present DVC side information generation based on a new method of optical flow driven frame interpolation, where a highly optimized TV-L1 algorithm is used for the flow calculations and combine three flows. Thereafter methods for exploiting the error-correcting capabilities of the LDPCA code in DVC are investigated. The proposed frame interpolation includes a symmetric flow constraint to the standard forward-backward frame interpolation scheme, which improves quality and handling of large motion. The three flows are combined in one solution. The proposed frame interpolation method consistently outperforms an overlapped block motion compensation scheme and a previous TV-L1 optical flow frame interpolation method with an average PSNR improvement of 1.3 dB and 2.3 dB respectively. For a GOP size of 2, an average bitrate saving of more than 40% is achieved compared to DISCOVER on Wyner-Ziv frames. In addition we also exploit and investigate the internal error-correcting capabilities of the LDPCA code in order to make it more robust to errors. We investigate how to achieve this goal by only modifying the decoding. One of approaches is to use bit flipping; alternatively one can modify the parity check matrix of the LDPCA. Different schemes known from LDPC codes are considered and evaluated in the LDPCA setting. Results show that the performance depend heavily on the type of channel used and on the quality of the Side Information.