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Thippeswamy, G.R.
- Quality of Video Rendering Techniques Using Artificial Intelligence
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Authors
Affiliations
1 Government B.Ed. Training College Kalinga, India., IN
2 Department of Computer Science and Engineering, Don Bosco Institute of Technology, India., IN
3 Department of Information Science and Engineering, RR Institute of Technology, India., IN
4 Department of Electronics and Telecommunication Engineering, JSPM’s Bhivarabai Sawant Institute of Technology and Research, India., IN
1 Government B.Ed. Training College Kalinga, India., IN
2 Department of Computer Science and Engineering, Don Bosco Institute of Technology, India., IN
3 Department of Information Science and Engineering, RR Institute of Technology, India., IN
4 Department of Electronics and Telecommunication Engineering, JSPM’s Bhivarabai Sawant Institute of Technology and Research, India., IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 3 (2023), Pagination: 2940-2946Abstract
In this paper, we propose a novel method that makes use of artificial intelligence to determine in a quick and accurate manner which bitrate ladder is best suited to each specific video scenario. This method is included as part of our overall contribution to this body of research. To accomplish fast entropy-based scene recognition using the artificial intelligence technique, a CNN model is utilised as part of the overall strategy. We were able to significantly reduce the amount of processing time necessary to recognise the scenes because we were dealing with versions of the video sequences that had both a lower quality and a lower bitrate. This allowed us to work more quickly. We first generated a training dataset that was large enough to train a convolutional neural network utilising the x264 video codec, and then we used that dataset to generate multiple encodings with varying bitrates, presets, and resolutions. The training dataset was created using the x264 video codec. As a result of the research that we carried out, we concluded that a particular collection of input features for the CNN model can be used to acquire a more accurate prediction of the level of video quality that will be produced. By predicting the PSNR quality measure for the segments, the suggested CNN model brings down the MAE and MSE to 0.2 and 0.05, respectively. This is accomplished by reducing the number of segments. This serves to reduce the amount of error overall.Keywords
Artificial Intelligence, Convolutional Neural Network, Video Quality Enhancement, Video Rendering.
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