Improving Video Quality by Predicting Inter-Frame Residuals Based on an Additive 3d-Cnn Model

2021 ◽  
Author(s):  
Hamid Azadegan ◽  
Ali Asghar Beheshti Shirazi
Keyword(s):  
Video Quality ◽  
3D Cnn ◽  
Inter Frame

SSIM-Based Distortion Estimation for Optimized Video Transmission over Inherently Noisy Channels

2016 ◽  
Vol 7 (3) ◽  
pp. 34-52 ◽  
Author(s):  
Arun Sankisa ◽  
Katerina Pandremmenou ◽  
Peshala V. Pahalawatta ◽  
Lisimachos P. Kondi ◽  
Aggelos K. Katsaggelos
Keyword(s):  
Video Transmission ◽  
Video Quality ◽  
Structural Similarity ◽  
Streaming Video ◽  
End User ◽  
Noisy Channels ◽  
Regression Framework ◽  
Improved Performance ◽  
Inter Frame ◽  
Distortion Estimate

The authors present two methods for examining video quality using the Structural Similarity (SSIM) index: Iterative Distortion Estimate (IDE) and Cumulative Distortion using SSIM (CDSSIM). In the first method, three types of slices are iteratively reconstructed frame-by-frame for three different combinations of packet loss and the resulting distortions are combined using their probabilities to give the total expected distortion. In the second method, a cumulative measure of the overall distortion is computed by summing the inter-frame propagation impact to all frames affected by a slice loss. Furthermore, the authors develop a No-Reference (NR) sparse regression framework for predicting the CDSSIM metric to circumvent the real-time computational complexity in streaming video applications. The two methods are evaluated in resource allocation and packet prioritization schemes and experimental results show improved performance and better end-user quality. The accuracy of the predicted CDSSIM values is studied using standard performance measures and a Quartile-Based Prioritization (QBP) scheme.

SSIM-Based Distortion Estimation for Optimized Video Transmission over Inherently Noisy Channels

Biometrics ◽  
2017 ◽  
pp. 690-709
Author(s):  
Arun Sankisa ◽  
Katerina Pandremmenou ◽  
Peshala V. Pahalawatta ◽  
Lisimachos P. Kondi ◽  
Aggelos K. Katsaggelos
Keyword(s):  
Performance Measures ◽  
Video Transmission ◽  
Video Quality ◽  
Structural Similarity ◽  
End User ◽  
Noisy Channels ◽  
Regression Framework ◽  
Improved Performance ◽  
Inter Frame ◽  
Distortion Estimate

The authors present two methods for examining video quality using the Structural Similarity (SSIM) index: Iterative Distortion Estimate (IDE) and Cumulative Distortion using SSIM (CDSSIM). In the first method, three types of slices are iteratively reconstructed frame-by-frame for three different combinations of packet loss and the resulting distortions are combined using their probabilities to give the total expected distortion. In the second method, a cumulative measure of the overall distortion is computed by summing the inter-frame propagation impact to all frames affected by a slice loss. Furthermore, the authors develop a No-Reference (NR) sparse regression framework for predicting the CDSSIM metric to circumvent the real-time computational complexity in streaming video applications. The two methods are evaluated in resource allocation and packet prioritization schemes and experimental results show improved performance and better end-user quality. The accuracy of the predicted CDSSIM values is studied using standard performance measures and a Quartile-Based Prioritization (QBP) scheme.

Video Stabilization Algorithm Based on Background Feature Points Matching

2015 ◽  
Vol 738-739 ◽  
pp. 690-693
Author(s):  
Shu Jiao Ji ◽  
Ming Zhu ◽  
Yan Min Lei
Keyword(s):  
Motion Estimation ◽  
Video Quality ◽  
Video Stabilization ◽  
Feature Points ◽  
Global Motion ◽  
Global Motion Estimation ◽  
Affine Transform ◽  
Feature Points Matching ◽  
Inter Frame ◽  
Background Feature

Global motion estimation between two successive frames is important to the process of video stabilization. In the proposed approach, the estimation of global motion was based on the background feature points (BFPS). First, feature points (FPS) were collected from the input video by FAST operator; second, feature point’s descriptor and matching were based on FREAK operator.The M-SAC is used to classify the BFPS. Last, the six parameters of the affine transform model to calculate the interframe motion estimation vector. The experiment results show that he proposed method can stabilize inter-frame jitter, in the meanwhile, it improve the video quality effectively.

Objective Video Quality Method Based on Mutual Information and Human Visual System

2012 ◽  
Vol 58 (2) ◽  
pp. 147-152
Author(s):  
Michal Mardiak ◽  
Jaroslav Polec
Keyword(s):  
Mutual Information ◽  
Visual System ◽  
Human Visual System ◽  
Quality Evaluation ◽  
Video Quality ◽  
Evaluation Criteria ◽  
Test Sequence ◽  
Quality Metric ◽  
Two Stages ◽  
Video Quality Metric

Objective Video Quality Method Based on Mutual Information and Human Visual SystemIn this paper we present the objective video quality metric based on mutual information and Human Visual System. The calculation of proposed metric consists of two stages. In the first stage of quality evaluation whole original and test sequence are pre-processed by the Human Visual System. In the second stage we calculate mutual information which has been utilized as the quality evaluation criteria. The mutual information was calculated between the frame from original sequence and the corresponding frame from test sequence. For this testing purpose we choose Foreman video at CIF resolution. To prove reliability of our metric were compared it with some commonly used objective methods for measuring the video quality. The results show that presented objective video quality metric based on mutual information and Human Visual System provides relevant results in comparison with results of other objective methods so it is suitable candidate for measuring the video quality.

The Influence of Video Quality on Sponsorship Effects

2018 ◽  
Vol 23 (2) ◽  
pp. 97-114
Author(s):  
Sanghak Lee ◽  
Paul M Pedersen
Keyword(s):  
Video Quality
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