Visual Quality Evaluation of Image Object Segmentation: Subjective Assessment and Objective Measure

2015 ◽  
Vol 24 (12) ◽  
pp. 5033-5045 ◽  
Author(s):  
Ran Shi ◽  
King Ngi Ngan ◽  
Songnan Li ◽  
Raveendran Paramesran ◽  
Hongliang Li
Keyword(s):  
Quality Evaluation ◽  
Object Segmentation ◽  
Objective Measure ◽  
Visual Quality ◽  
Subjective Assessment ◽  
Image Object

scholarly journals Adaptive Reservation of Network Resources According to Video Classification Scenes

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1949
Author(s):  
Lukas Sevcik ◽  
Miroslav Voznak
Keyword(s):  
User Satisfaction ◽  
Quality Evaluation ◽  
Spatial Information ◽  
Service Providers ◽  
Video Quality ◽  
Subjective Assessment ◽  
Video Sequences ◽  
Network Resources ◽  
Proposed Model

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.

Video Objective Quality Evaluation System Based on the Visual Saliency Map

2013 ◽  
Vol 411-414 ◽  
pp. 1362-1367 ◽  
Author(s):  
Qing Lan Wei ◽  
Yuan Zhang
Keyword(s):  
Evaluation System ◽  
Quality Evaluation ◽  
Objective Assessment ◽  
Visual Saliency ◽  
Objective Evaluation ◽  
Subjective Assessment ◽  
Saliency Map ◽  
Assessment Scores ◽  
Objective Quality ◽  
Quality Evaluation System

This paper presents the thoughts about application of saliency map to the video objective quality evaluation system. It computes the SMSE and SPSNR values as the objective assessment scores according to the saliency map, and compares with conditional objective evaluation methods as PSNR and MSE. Experimental results demonstrate that this method can well fit the subjective assessment results.

Visual-quality evaluation methodology for multiview displays

Displays ◽  
2012 ◽  
Vol 33 (2) ◽  
pp. 103-112 ◽  
Author(s):  
Atanas Boev ◽  
Robert Bregovic ◽  
Atanas Gotchev
Keyword(s):  
Quality Evaluation ◽  
Visual Quality ◽  
Evaluation Methodology

Naturalness and interestingness of test images for visual quality evaluation

2011 ◽  
Author(s):  
Raisa Halonen ◽  
Stina Westman ◽  
Pirkko Oittinen
Keyword(s):  
Quality Evaluation ◽  
Visual Quality
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