Chen Chao

HTTP video streaming is in wide use to deliver video over the Internet. With HTTP adaptive steaming, a video playback dynamically selects a video stream from a pre-encoded representation based on available bandwidth and viewport (screen) size. The viewer's video quality is therefore influenced by the encoded bitrates. We minimize the average delivered bitrate subject to a quality lower bound on a per-chunk basis by modeling the probability that a player selects a particular encoding. Through… Show more

HTTP video streaming is in wide use to deliver video over the Internet. With HTTP adaptive steaming, a video playback dynamically selects a video stream from a pre-encoded representation based on available bandwidth and viewport (screen) size. The viewer's video quality is therefore influenced by the encoded bitrates. We minimize the average delivered bitrate subject to a quality lower bound on a per-chunk basis by modeling the probability that a player selects a particular encoding. Through simulation and real-world experiments, the proposed method saves 9.6% of bandwidth while average delivered video quality comparing with state of the art while keeping average delivered video quality. Show less

No-Reference (NR) video quality assessment (VQA) models are gaining popularity as they offer scope for broader applicability to user-uploaded video-centric services such as YouTube and Facebook, where the pristine references are unavailable. However, there are few, well-performing NR-VQA models owing to the difficulty of the problem. We propose a novel NR video quality predictor that solely relies on the `quality-aware' natural statistical models in the space-time domain. The proposed quality… Show more

No-Reference (NR) video quality assessment (VQA) models are gaining popularity as they offer scope for broader applicability to user-uploaded video-centric services such as YouTube and Facebook, where the pristine references are unavailable. However, there are few, well-performing NR-VQA models owing to the difficulty of the problem. We propose a novel NR video quality predictor that solely relies on the `quality-aware' natural statistical models in the space-time domain. The proposed quality predictor called Self-reference based LEarning-free Evaluator of Quality (SLEEQ) consists of three components: feature extraction in the spatial and temporal domains, motion-based feature fusion, and spatial-temporal feature pooling to derive a single quality score for a given video. SLEEQ achieves higher than 0.9 correlation with the subjective video quality scores on tested public databases and thus outperforms the existing NR VQA models. Show less

Assessing the perceptual quality of video is critical for monitoring and optimizing video processing pipelines. In this paper, we focus on predicting the perceptual quality for videos distorted by noise. Existing video quality metrics are generally focus on ``white", i.e., spatially un-correlated noise. However, white noise is very rare in realistic videos. Based on our analysis of the noise correlation patterns on a large and comprehensive video set, we build a video database that simulates… Show more

Assessing the perceptual quality of video is critical for monitoring and optimizing video processing pipelines. In this paper, we focus on predicting the perceptual quality for videos distorted by noise. Existing video quality metrics are generally focus on ``white", i.e., spatially un-correlated noise. However, white noise is very rare in realistic videos. Based on our analysis of the noise correlation patterns on a large and comprehensive video set, we build a video database that simulates the commonly encountered noise patterns. Using the database, we develop a perceptual quality metric that explicitly incorporates the noise pattern in quality prediction. Experimental results show that the proposed algorithm presents very high correlation with the perceptual quality of noisy videos. Show less

Banding is a common video artifact caused by compressing low texture regions with coarse quantization. Relatively few previous attempts exist to address banding and none incorporate subjective testing for calibrating the measurement. In this paper, we propose a novel metric that incorporates both edge length and contrast across the edge to measure video banding. We further introduce both reference and non-reference metrics. Our results demonstrate that the new metrics have a very high… Show more

Banding is a common video artifact caused by compressing low texture regions with coarse quantization. Relatively few previous attempts exist to address banding and none incorporate subjective testing for calibrating the measurement. In this paper, we propose a novel metric that incorporates both edge length and contrast across the edge to measure video banding. We further introduce both reference and non-reference metrics. Our results demonstrate that the new metrics have a very high correlation with subjective assessment and certainly outperforms PSNR, SSIM, and VQM. Show less

Adaptive bitrate (ABR) streaming is one enabling technology for video streaming over modern throughput-varying communication networks. A widely used ABR streaming method is to adapt the video bitrate to channel throughput by dynamically changing the video resolution. Since videos have different rate-quality performances at different resolutions, such ABR strategy can achieve better rate-quality trade-off than single resolution ABR streaming. The key problem for resolution switched ABR is to… Show more

Adaptive bitrate (ABR) streaming is one enabling technology for video streaming over modern throughput-varying communication networks. A widely used ABR streaming method is to adapt the video bitrate to channel throughput by dynamically changing the video resolution. Since videos have different rate-quality performances at different resolutions, such ABR strategy can achieve better rate-quality trade-off than single resolution ABR streaming. The key problem for resolution switched ABR is to work out the bitrate appropriate at each resolution. In this paper, we investigate optimal strategies to estimate this bitrate using both quantitative and subjective quality assessment. We use the design of 2K and 4K bitrates as an example of the performance of this strategy. We introduce strategies for selecting an appropriate corpus for subjective assessment and find that at this high resolution there is good agreement between quantitative and subjective analysis. Show less

Adapting video data rate during streaming can effectively reduce the risk of playback interruptions caused by channel throughput fluctuations. The variations in rate, however, also introduce video quality fluctuations and thus potentially affects viewers' Quality of Experience (QoE). We show how the QoE of video users can be improved by rate adaptation and admission control. We conducted a subjective study wherein we found that viewers' QoE was strongly correlated with the empirical cumulative… Show more

Adapting video data rate during streaming can effectively reduce the risk of playback interruptions caused by channel throughput fluctuations. The variations in rate, however, also introduce video quality fluctuations and thus potentially affects viewers' Quality of Experience (QoE). We show how the QoE of video users can be improved by rate adaptation and admission control. We conducted a subjective study wherein we found that viewers' QoE was strongly correlated with the empirical cumulative distribution function (eCDF) of the predicted video quality. Based on this observation, we propose a rate-adaptation algorithm that can incorporate QoE constraints on the empirical cumulative quality distribution per user. We then propose a threshold-based admission control policy to block users whose empirical cumulative quality distribution is not likely to satisfy their QoE constraint. We further devise an online adaptation algorithm to automatically optimize the threshold. Extensive simulation results show that the proposed scheme can reduce network resource consumption by 40% over conventional average-quality maximized rate-adaptation algorithms. Show less

We conducted a subjective study wherein we found that viewers’ Quality of Experience (QoE) was strongly correlated with the empirical cumulative distribution function (eCDF) of the predicted video quality. Based on this observation, we propose a rate-adaptation algorithm that can incorporate QoE constraints on the empirical cumulative quality distribution per user. Simulation results show that the proposed technique can reduce network resource consumption by 29% over conventional… Show more

We conducted a subjective study wherein we found that viewers’ Quality of Experience (QoE) was strongly correlated with the empirical cumulative distribution function (eCDF) of the predicted video quality. Based on this observation, we propose a rate-adaptation algorithm that can incorporate QoE constraints on the empirical cumulative quality distribution per user. Simulation results show that the proposed technique can reduce network resource consumption by 29% over conventional average-quality maximized rate-adaptation algorithms. Show less

Newly developed hypertext transfer protocol (HTTP)-based video streaming technologies enable flexible rate-adaptation under varying channel conditions. Accurately predicting the users' quality of experience (QoE) for rate-adaptive HTTP video streams is thus critical to achieve efficiency. An important aspect of understanding and modeling QoE is predicting the up-to-the-moment subjective quality of a video as it is played, which is difficult due to hysteresis effects and nonlinearities in human… Show more

Newly developed hypertext transfer protocol (HTTP)-based video streaming technologies enable flexible rate-adaptation under varying channel conditions. Accurately predicting the users' quality of experience (QoE) for rate-adaptive HTTP video streams is thus critical to achieve efficiency. An important aspect of understanding and modeling QoE is predicting the up-to-the-moment subjective quality of a video as it is played, which is difficult due to hysteresis effects and nonlinearities in human behavioral responses. This paper presents a Hammerstein-Wiener model for predicting the time-varying subjective quality (TVSQ) of rate-adaptive videos. To collect data for model parameterization and validation, a database of longer duration videos with time-varying distortions was built and the TVSQs of the videos were measured in a large-scale subjective study. The proposed method is able to reliably predict the TVSQ of rate adaptive videos. Since the Hammerstein-Wiener model has a very simple structure, the proposed method is suitable for online TVSQ prediction in HTTP-based streaming. Show less

We propose two scheduling algorithms that seek to optimize the quality of scalably coded videos that have been stored at a video server before transmission. The first scheduling algorithm is derived from a Markov decision process (MDP) formulation developed here. We model the dynamics of the channel as a Markov chain and reduce the problem of dynamic video scheduling to a tractable Markov decision problem over a finite-state space. Based on the MDP formulation, a near-optimal scheduling policy… Show more

We propose two scheduling algorithms that seek to optimize the quality of scalably coded videos that have been stored at a video server before transmission. The first scheduling algorithm is derived from a Markov decision process (MDP) formulation developed here. We model the dynamics of the channel as a Markov chain and reduce the problem of dynamic video scheduling to a tractable Markov decision problem over a finite-state space. Based on the MDP formulation, a near-optimal scheduling policy is computed that minimizes the mean square error. Using insights taken from the development of the optimal MDP-based scheduling policy, the second proposed scheduling algorithm is an online scheduling method that only requires easily measurable knowledge of the channel dynamics, and is thus viable in practice. Simulation results show that the performance of both scheduling algorithms is close to a performance upper bound also derived in this paper. Show less

Newly developed HTTP-based video streaming technology enables flexible rate-adaptation in varying channel conditions. The users' Quality of Experience (QoE) of rate-adaptive HTTP video streams, however, is not well understood. Therefore, designing QoE-optimized rate-adaptive video streaming algorithms remains a challenging task. An important aspect of understanding and modeling QoE is to be able to predict the up-to-the-moment subjective quality of video as it is played. We propose a dynamic… Show more

Newly developed HTTP-based video streaming technology enables flexible rate-adaptation in varying channel conditions. The users' Quality of Experience (QoE) of rate-adaptive HTTP video streams, however, is not well understood. Therefore, designing QoE-optimized rate-adaptive video streaming algorithms remains a challenging task. An important aspect of understanding and modeling QoE is to be able to predict the up-to-the-moment subjective quality of video as it is played. We propose a dynamic system model to predict the time-varying subjective quality (TVSQ) of rate-adaptive videos that is transported over HTTP. For this purpose, we built a video database and measured TVSQ via a subjective study. A dynamic system model is developed using the database and the measured human data. We show that the proposed model can effectively predict the TVSQ of rate-adaptive videos in an online manner, which is necessary to be able to conduct QoE-optimized online rate-adaptation for HTTP-based video streaming. Show less

We study the problem of adaptive video data scheduling over wireless channels. We prove that, under certain assumptions, adaptive video scheduling can be reduced to a Markov decision process over a finite state space. Therefore, the scheduling policy can be optimized via standard stochastic control techniques using a Markov decision formulation. Simulation results show that significant performance improvement can be achieved over heuristic transmission schemes.

In all existing codec designs for asymmetric Slepian-Wolf coding (SWC), it is assumed that the source sequence is i.i.d and equiprobable. When it comes to more complex source statistics, the encoder should firstly remove the redundancy within the source. However, this increases the complexity of the encoder. In this paper, we propose an asymmetric SWC scheme which explores the redundancy of the binary finite memory source (FMS) at the decoder. Specifically, inspired by the Burrows-Wheeler… Show more

In all existing codec designs for asymmetric Slepian-Wolf coding (SWC), it is assumed that the source sequence is i.i.d and equiprobable. When it comes to more complex source statistics, the encoder should firstly remove the redundancy within the source. However, this increases the complexity of the encoder. In this paper, we propose an asymmetric SWC scheme which explores the redundancy of the binary finite memory source (FMS) at the decoder. Specifically, inspired by the Burrows-Wheeler transform (BWT)-based source-controlled channel decoding algorithm proposed, we iteratively apply the LDPC decoding and BWT to the side information. In our codec implementation, the encoder is identical to the conventional LDPC-based SWC encoder. At the decoder, conventional LDPC-based SWC decoding algorithm and Burrows-Wheeler transform (BWT) are iteratively applied to the side information for decoding. BWT can asymptotically permute a FMS into a piece-wise i.i.d binary sequence. In other words, by applying BWT to the decoder side information, the redundancy in the memory is transformed into the redundancy in the marginal distributions of the output i.i.d segments. The marginal distributions of every i.i.d segment can be used as the a priori information for SWC decoding. To explore the marginal distribution, a segmentation algorithm is employed to adaptively partition the output sequence of BWT into i.i.d segments. The bias parameter of each segment is then empirically computed. Using these parameters, the a priori information of the FMS source can be derived and incorporated in the next iteration of SWC decoding. Experimental results show that our scheme performs significantly better than the scheme which does not utilize the a priori information for decoding. Show less