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.
Highlights
The growing interest in real-time services, especially video transmission over Internet Protocol (IP) packet networks, prompts analyses of these services and their behavior
Increased user demands and high-quality service expectations have encouraged operators to improve their systems. This need is stimulated by ultra-high video resolution (4K/8K). In such a flexible and agile background, it is essential to deal with a new model or approaches
The standard deviation of the pixels is calculated for each filtered frame. This operation is repeated for each frame in the video sequence, and the result is the highest value in the time series representing the spatial information (SI) content of the selected scene (Equation (1)) [16]: SI = maxtime[stdspace[Sobel( fn)]]
Summary
The growing interest in real-time services, especially video transmission over Internet Protocol (IP) packet networks, prompts analyses of these services and their behavior. Intelligent transport systems, body-worn cameras, and many others contribute to the amount of data They need to be processed as efficiently as possible in terms of video compression by maintaining the highest possible quality with minimal network resource use. This need is stimulated by ultra-high video resolution (4K/8K) In such a flexible and agile background, it is essential to deal with a new model or approaches (e.g., some metrics evaluating user satisfaction, or setting a suitable bitrate to match with compression standard and quality). The proposed model brings the advantage of a variable bitrate that changes by type of the scene and simulations of the subjective quality of the scene using a classifier based on the artificial intelligence network that was published in [5] It keeps a constant quality for the end users and safe bandwidth for the providers. Our model gives recommendations for selecting a suitable bitrate for individual video scenes to achieve subjective quality
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