19.4: Improvement of Moving-Video Image Quality on PDPs by Reducing the Dynamic False Contour

1998 ◽  
Vol 29 (1) ◽  
pp. 287 ◽  
Author(s):  
T. Shigeta ◽  
N. Saegusa ◽  
H. Honda ◽  
T. Nagakubo ◽  
T. Akiyama
Keyword(s):  
Image Quality ◽  
Video Image ◽  
Dynamic False Contour

Improving video image quality using automated wavelet-based image addition

1999 ◽  
Author(s):  
Thierry Daubos ◽  
Zeno J. Geradts ◽  
Jean-Luc Starck ◽  
Jonathan G. Campbell ◽  
Fionn D. Murtagh
Keyword(s):  
Image Quality ◽  
Video Image

scholarly journals Image Quality Evaluation of Sanda Sports Video Based on BP Neural Network Perception

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Kai Fan ◽  
Xiaoye Gu
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Evaluation Method ◽  
Video Image ◽  
Time Analysis ◽  
Sports Video ◽  
Real Time Analysis ◽  
Video Images ◽  
Motion Video

In the special sports camera, there are subframes. A lens is composed of multiple frames. It will be unclear if a frame is cut out. The definition of video screenshots lies in the quality of video. To get clear screenshots, we need to find clear video. The purpose of this paper is to analyze and evaluate the quality of sports video images. Through the semantic analysis and program design of video using computer language, the video images are matched with the data model constructed by research, and the real-time analysis of sports video images is formed, so as to achieve the real-time analysis effect of sports techniques and tactics. In view of the defects of rough image segmentation and high spatial distortion rate in current sports video image evaluation methods, this paper proposes a sports video image evaluation method based on BP neural network perception. The results show that the optimized algorithm can overcome the slow convergence of weights of traditional algorithm and the oscillation in error convergence of variable step size algorithm. The optimized algorithm will significantly reduce the learning error of neural network and the overall error of network quality classification and greatly improve the accuracy of evaluation. Sanda motion video image quality evaluation method based on BP (back propagation) neural network perception has high spatial accuracy, good noise iteration performance, and low spatial distortion rate, so it can accurately evaluate Sanda motion video image quality.

25.4: Effect of MPEG-2 Compression Parameters on Perceived Video Image Quality

2006 ◽  
Vol 37 (1) ◽  
pp. 1234 ◽  
Author(s):  
Nele Van den Ende ◽  
Reinder Haakma ◽  
Lydia Meesters
Keyword(s):  
Image Quality ◽  
Video Image

A Improved Method of Enhancement of Video Image in the Coal Mine

2012 ◽  
Vol 490-495 ◽  
pp. 1898-1902
Author(s):  
Meng Ling Zhao
Keyword(s):  
Image Quality ◽  
Coal Mine ◽  
Contrast Ratio ◽  
Video Image ◽  
Experimental Results ◽  
Improved Method ◽  
Monitor Screen ◽  
Feasible Method ◽  
Transmission Process ◽  
Communication Environment

Insufficient space and poor light down the coal mine resulted in many problems to the image on the monitor screen. In view of the problem of low resolution, unclear gradation, roughness and easy to interfere in the transmission process in bad communication environment, According to the characteristics of the image, researched to use the method of combining the algorithm of improving the traditional filter with the enhancement of contrast ratio to improve image. The experimental results indicate the method can make the underground video image quality improved and is a feasible method.

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