Spatiotemporal Video Quality Assessment Method via Multiple Feature Mappings

Progressed video quality assessment (VQA) methods aim to evaluate the perceptual quality of videos in many applications but often prompt to increase computational complexity. Problems derive from the complexity of the distorted videos that are of significant concern in the communication industry, as well as the spatial-temporal content of the two-fold (spatial and temporal) distortion. Therefore, the findings of the study indicate that the information in the spatiotemporal slice (STS) images are useful in measuring video distortion. This paper mainly focuses on developing on a full reference video quality assessment algorithm estimator that integrates several features of spatiotemporal slices (STSS) of frames to form a high-performance video quality. This research work aims to evaluate video quality by utilizing several VQA databases by the following steps: (1) we first arrange the reference and test video sequences into a spatiotemporal slice representation. A collection of spatiotemporal feature maps were computed on each reference-test video. These response features are then processed by using a Structural Similarity (SSIM) to form a local frame quality.  (2) To further enhance the quality assessment, we combine the spatial feature maps with the spatiotemporal feature maps and propose the VQA model, named multiple map similarity feature deviation (MMSFD-STS). (3) We apply a sequential pooling strategy to assemble the quality indices of frames in the video quality scoring. (4) Extensive evaluations on video quality databases show that the proposed VQA algorithm achieves better/competitive performance as compared with other state- of- the- art methods

SpatioTemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment

Perceptual video quality assessment models are either frame-based or video-based, i.e., they apply spatiotemporal filtering or motion estimation to capture temporal video distortions. Despite their good performance on video quality databases, video-based approaches are time-consuming and harder to efficiently deploy. To balance between high performance and computational efficiency, Netflix developed the Video Multi-method Assessment Fusion (VMAF) framework, which integrates multiple quality-aware features to predict video quality. Nevertheless, this fusion framework does not fully exploit temporal video quality measurements which are relevant to temporal video distortions. To this end, we propose two improvements to the VMAF framework: SpatioTemporal VMAF and Ensemble VMAF. Both algorithms exploit efficient temporal video features which are fed into a single or multiple regression models. To train our models, we designed a large subjective database and evaluated the proposed models against state-of-the-art approaches. The compared algorithms will be made available as part of the open source package in https://github.com/Netflix/vmaf

Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach

HTTP based adaptive video streaming has become a popular choice of streaming due to the reliable transmission and the flexibility offered to adapt to varying network conditions. However, due to rate adaptation in adaptive streaming, the quality of the videos at the client keeps varying with time depending on the end-to-end network conditions. Further, varying network conditions can lead to the video client running out of playback content resulting in rebuffering events. These factors affect the user satisfaction and cause degradation of the user quality of experience (QoE). It is important to quantify the perceptual QoE of the streaming video users and monitor the same in a continuous manner so that the QoE degradation can be minimized. However, the continuous evaluation of QoE is challenging as it is determined by complex dynamic interactions among the QoE influencing factors. Towards this end, we present LSTM-QoE, a recurrent neural network based QoE prediction model using a Long Short-Term Memory (LSTM) network. The LSTM-QoE is a network of cascaded LSTM blocks to capture the nonlinearities and the complex temporal dependencies involved in the time varying QoE. Based on an evaluation over several publicly available continuous QoE databases, we demonstrate that the LSTM-QoE has the capability to model the QoE dynamics effectively. We compare the proposed model with the state-of-the-art QoE prediction models and show that it provides superior performance across these databases. Further, we discuss the state space perspective for the LSTM-QoE and show the efficacy of the state space modeling approaches for QoE prediction