Adaptive Reservation of Network Resources According to Video Classification Scenes

Abstract

Video quality evaluation needs a combined approach that includes subjective and objective metrics, testing, and monitoring of the network. This paper deals with the novel approach of mapping quality of service (QoS) to quality of experience (QoE) using QoE metrics to determine user satisfaction limits, and applying QoS tools to provide the minimum QoE expected by users. Our aim was to connect objective estimations of video quality with the subjective estimations. A comprehensive tool for the estimation of the subjective evaluation is proposed. This new idea is based on the evaluation and marking of video sequences using the sentinel flag derived from spatial information (SI) and temporal information (TI) in individual video frames. The authors of this paper created a video database for quality evaluation, and derived SI and TI from each video sequence for classifying the scenes. Video scenes from the database were evaluated by objective and subjective assessment. Based on the results, a new model for prediction of subjective quality is defined and presented in this paper. This quality is predicted using an artificial neural network based on the objective evaluation and the type of video sequences defined by qualitative parameters such as resolution, compression standard, and bitstream. Furthermore, the authors created an optimum mapping function to define the threshold for the variable bitrate setting based on the flag in the video, determining the type of scene in the proposed model. This function allows one to allocate a bitrate dynamically for a particular segment of the scene and maintains the desired quality. Our proposed model can help video service providers with the increasing the comfort of the end users. The variable bitstream ensures consistent video quality and customer satisfaction, while network resources are used effectively. The proposed model can also predict the appropriate bitrate based on the required quality of video sequences, defined using either objective or subjective assessment.

UHD Database Focus on Smart Cities and Smart Transport

“…In our evaluation, we use FF objective methods (SSIM, MS-SSIM, PSNR, and VMAF). A more detailed description can be found in our previous publications [39,40]. As a subjective metric, we use the non-referential ACR method, because, in this case, the video is compared only based on the seen video sequence and not by comparison with a reference.…”

“…This metric summarizes ratings that are averaged on a scale from 1, which is the worst quality, to 5, which represents excellent quality. For more information, see our publication [40].…”

“…The temporal information is derived from changes in the brightness of the same pixels in successive frames, while the spatial information is obtained from the Sobel filter for the luminance component and the subsequent calculation of the standard deviation of the pixels. More details can be found in our publication [40]. Table 2 shows the characterization of the reference sequences based on the SI and TI values, which highlights the diversity of the individual sequences in terms of spatial and temporal information.…”

Deep Multi-Scale Features Fusion for Effective Violence Detection and Control Charts Visualization

“…The traditional sensing and functionalities of surveillance systems are replaced by automated techniques that are used to analyze the video data from CCTV cameras by considering different features and motion patterns to decide the nature of the events occurring in real-world environments. These are video classification [ 2 ] techniques to detect and to recognize different types of actions and events in surveillance videos [ 3 ], while broadly, the activities cover normal motion patterns such as walking, and abnormal actions such as punching, etc. In real-world surveillance environments, abnormal events carry more importance, as they are directly related to the safety of the citizens.…”