Reduced-reference Video Quality Assessment Research Articles

A reduced reference video quality assessment of H.264 and mpeg2 codec based on sharpness metric

AbstractIn this paper, we propose a new perceptually significant video quality metric for the H.264/Motion Picture Expert Group (MPEG)‐4 Advanced Video Coding (AVC) and MPEG2 standard. Our method operates in the spatial domain by using the Sobel filter. The proposed approach does not require a high computational complexity and can be suitable for real‐time evaluation. We evaluate the performance of the proposed method by using three Common Intermediate Format sequences at different compression rates. The comparison of the obtained results is made with some video quality models using “LIVE”, “IVP” and “IRCCyN/IVC 1080i” databases. The performance metrics, i.e. Pearson and Spearman correlation coefficients, indicate that the proposed method gives a good performance in H264 and MPEG2 codec distortions with the three databases comparing with other models.

Reduced-reference video quality assessment using a static video pattern

A reduced-reference video quality assessment (VQA) method was proposed by using structural similarity (SSIM) index as a tool to extract features from both the original and the target video sequences, using a reference video pattern. The method is suitable for monitoring the video quality in real time and across the service provision chain. The performance of the proposed method was evaluated using a large experimental set of reference and nonreference video sequences and achieves an accuracy higher than 2.56% in comparison to SSIM. Additionally, comparison to subjectively evaluated scores of Laboratory for Image and Video Engineering video quality dataset, based on difference mean opinion scores, shows that the performance of the proposed method is within the range of the full reference VQA methods.

Video Quality Assessment by Reduced Reference Spatio-Temporal Entropic Differencing

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.

A Reduced-Reference Video Quality Assessment Method Based on the Activity-Difference of DCT Coefficients

A Reduced-Reference Video Quality Assessment Method Based on the Activity-Difference of DCT Coefficients

Reduced-Reference Video Quality Assessment of Compressed Video Sequences

In this paper, a novel reduced-reference (RR) video quality assessment (VQA) is proposed by exploiting the spatial information loss and the temporal statistical characteristics of the interframe histogram. From the spatial perspective, an energy variation descriptor (EVD) is proposed to measure the energy change of each individual encoded frame, which results from the quantization process. Besides depicting the energy change, EVD can further simulate the texture masking property of the human visual system (HVS). From the temporal perspective, the generalized Gaussian density (GGD) function is employed to capture the natural statistics of the interframe histogram distribution. The city-block distance (CBD) is used to calculate the histogram distance between the original video sequence and the encoded one. For simplicity, the difference image between adjacent frames is employed to characterize the temporal interframe relationship. By combining the spatial EVD together with the temporal CBD, an efficient RR VQA is developed. Evaluation on the subjective quality video database demonstrates that the proposed method outperforms the representative RR video quality metric and the full-reference VQAs, such as peak signal-to-noise ratio and structure similarity index in matching subjective ratings. This means that the proposed metric is more consistent with the HVS perception. Furthermore, as only a small number of RR features are extracted for representing the original video sequence (each frame requires only one parameter for describing EVD and three parameters for recording GGD), the RR features can be embedded into the video sequences or transmitted through the ancillary data channel, which can be used in the video quality monitoring system.

Reduced-Reference Video Quality Assessment Based on BPNeural Network Model for Packet Networks

This paper investigates quality monitoring of videos transmitted over packet networks. Our goal is to develop a methodology that is both simple and accurate to support quality assessment for videos over packet networks. For this purpose, this paper focus on the parameters that affect the quality of videos and uses back propagation Neural Networks (BP-NN) to mimic the way that human viewers assess the quality of videos transmitted over packet networks. Because network factors and motion change degree, that many people may ignore, are both important factors for video quality assessment, in this paper we propose a video quality assessment system considering packet loss rate (PLR), the motion change degree of video and subjective ratings as the inputs of back propagation Neural Networks (BP-NN) for training to get more precise video quality. Simulation result shows that our system can be used to measure the subjective video in real time with very good precision. © 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of ESEP 2011

Efficient Reduced-Reference Video Quality Meter

We present an efficient reduced-reference video quality assessment method based on local harmonic strength (LHS) feature. LHS is based on harmonics gain and loss information derived through the discriminative analysis of harmonics strength computed from gradient pictures. Harmonics gain and loss correspond with the two most prominent compression artifacts, namely blockiness and blurriness. To make the method effective for continuous monitoring of video quality, it is necessary to minimize the reduced-reference overhead data whilst keeping the accuracy of the quality meter sufficiently high. For the LHS-based method, we recommend quantization of the LHS feature, temporal sampling, and block selection method, either with or without segmentation-based block classification. A typical reduced-reference low overhead data rate (around 160-400 bps) with good prediction performance (0.86-0.88 Pearson correlations against mean opinion score) for broadcast type video sequences in VQEG Test Phase-I data sets has been achieved by the proposed method.

Reduced-Reference Video Quality Assessment Using Discriminative Local Harmonic Strength With Motion Consideration

This paper presents a reduced-reference objective picture quality measurement tool of compressed video. We have used a discriminative analysis of harmonic strength computed from edge-detected pictures to create harmonics gain and loss information that could be associated with the picture. The harmonics gain/loss are derived through the harmonic analysis of the compressed and source pictures to be incorporated in the reduced-reference video quality meter. This information corresponds with the two most prominent compression distortions, namely blockiness and blurriness. We have also studied the impact of motion in a video sequence on these compression distortions and the way they should be weighted and combined to give the best objective quality metric model. The model has been calibrated using several video sequences with dominant blockiness and blurriness. Validation of the model is performed by applying the model to the 50 Hz video sequences of VQEG Test Phase-I. Our results show that the proposed model achieves good correlations with the subjective evaluations of the VQEG datasets and its performance is comparable to those of the full-reference models in the literature.