No-reference Video Quality Assessment Model Research Articles

3D-DWT cross-band statistics and features for No-Reference Video Quality Assessment (NR-VQA)

This paper presents a robust, novel, and computationally efficient noise estimation-based NR-VQA model. It uses four novel sub-band features; namely cross-band statistics, sub-band kurtosis, sub-band energy ratios, and natural video statistics in three-dimensional discrete wavelet transform (3D-DWT) domain. As the HVS shows high sensitivity towards the structural and textural information, a three-dimensional gray level co-occurrence matrix (3D-GLCM) is used to select top 25% video blocks having maximal textural information. We validate the performance of the proposed method on five publicly available and widely used video quality databases. Extensive experiments show that the proposed model yields considerably better and robust performance than other contemporary state-of-the-art NR-VQA methods at lower computational costs.

No-Reference Video Quality Assessment Based on Visual Memory Modeling

Objective video quality assessment plays an important role in a variety of video processing applications such as video compression, transmission, visualization, and display. This paper proposes a no-reference video quality assessment model based on visual memory understanding. Inspired by the findings of neuroscience researchers, who argue there is a large overlap between the active human brain area when performing video quality assessment and saliency detection tasks, saliency maps are employed here to assist the quality assessment. To this end, we first generate the saliency maps using CLBP (Complete Local Binary Patterns) features of the residual frames. Then, a model of visual memory is created from the statistics of saliency maps. This is followed by learning the video quality from the visual memory, saliency, and frame features through a support vector regression pipeline. The experimental results on the state-of-the-art LIVE and SJTU video datasets indicate that the proposed no-reference video quality assessment algorithm is effective and performs statistically better than several other state-of-the-art approaches.

An improved model for no-reference image quality assessment and a no-reference video quality assessment model based on frame analysis

No-reference image quality assessment (NR-IQA) uses only the test image for its quality assessment, and as video is essentially comprised of image frames with additional temporal dimension, video quality assessment (VQA) requires a thorough understanding of image quality assessment metrics and models. Therefore, in order to identify features that deteriorate video quality, a fundamental analysis of spatial and temporal artifacts with respect to individual video frames needs to be performed. Existing IQA and VQA metrics are primarily for capturing few distortions and hence may not be good for all types of images and videos. In this paper, we propose an NR-IQA model by combining existing three methods (namely NIQE, BRISQUE and BLIINDS-II) using multi-linear regression. We also present a holistic no-reference video quality assessment (NR-VQA) model by exploring quantification of certain distortions like ringing, frame difference, blocking, clipping and contrast in video frames. For the proposed NR-IQA model, the results represent improved performance as compared to the state-of-the-art methods and it requires very low fraction of samples for training to provide a consistent accuracy over different training-to-testing ratios. The performance of NR-VQA model is examined using a simple neural network model to attain high value of goodness of fit.

Compressed-domain-based no-reference video quality assessment model considering fast motion and scene change

Due to the variability of wireless channel state, video quality monitoring became very important for guaranteeing users' Quality of Experience (QoE). QoE presents the overall perceptual quality of service from the subjective users' perspective. However, because of diverse characteristics of video content, Human Visual System (HVS) cannot give the same attention to whole scene simultaneously when facing video sequence. In this paper, we proposed a video quality assessment model by considering the influence of fast motion and scene change. The motion change contribution factor and scene change contribution factor are defined to quantify the characteristics of video content, which is closely related to the users' QoE. Based on G.1070, our proposed model considers the influential factors of loss nature of video coding, variability of practical network and video features. Also, the proposed model owns low computational complexity due to the compressed domain approach for the estimation of the model parameters. Therefore, the video quality is assessed without fully decoding the video stream. The performance of our proposed model has been compared with five existing models and the results also shown that our model has high prediction accuracy closing to human perception.

The Influencing Factors of Network Packet Loss's Long-Range Dependence has Impacts on the Packet Loss Rate

In order to better establish no-reference video quality assessment model considering the network packet loss and further gain a better QoE evaluation to meet the needs of the user’s, so we build NS2+ MyEvalvid simulation platform to study the scale characteristic of the network packet loss, scale characteristic of packet loss through the influence of packet loss rate to influence QoE. The experimental results show that, packet loss processes has long-range dependence, the number of superimposed source N, shape parameter, Hurst parameter, the output link speed have impacts on long-range dependence. We came to the conclusion that when superimposed source N is more, shape parameter is smaller, Hurst parameter is bigger, the output link speed is smaller, packet loss’s long range dependence is larger, packet loss rate is high.

Research on No-reference Video Quality Assessment Based on H.264/AVC Bitstream

It is an important part to efficiently evaluate the video quality for user experience in video systems. Traditional video evaluation criterion is strict for testing environment, and is not appropriate for real-time evaluation on network vid- eo. No-reference video quality assessment doesn't require any information of original video, which makes it meet the characteristics of high real-time evaluation on network video image quality and small resource reduction. By extracting key parameters distorted by characteristic video in H.264/AVC video stream flow including quantification parameter, en- coding rate and bits of each macro block of coding video, the paper establishes no-reference video quality assessment model from the space complexity and time complexity of video. For the model, not only the model is not complicated, and can meet the requirement of real-time quality assessment of video, the simulation test but also proves that the correlation between the algorithm and subjective assessment value is high, which meets visual perception feature of human eye.

No-Reference Video Quality Assessment Model for Distortion Caused by Packet Loss in the Real-Time Mobile Video Services

Packet loss will make severe errors due to the corruption of related video data. For most video streams, because the predictive coding structures are employed, the transmission errors in one frame will not only cause decoding failure of itself at the receiver side, but also propagate to its subsequent frames along the motion prediction path, which will bring a significant degradation of end-to-end video quality. To quantify the effects of packet loss on video quality, a no-reference objective quality assessment model is presented in this paper. Considering the fact that the degradation of video quality significantly relies on the video content, the temporal complexity is estimated to reflect the varying characteristic of video content, using the macroblocks with different motion activities in each frame. Then, the quality of the frame affected by the reference frame loss, by error propagation, or by both of them is evaluated, respectively. Utilizing a two-level temporal pooling scheme, the video quality is finally obtained. Extensive experimental results show that the video quality estimated by the proposed method matches well with the subjective quality.