| Autor: |
Challa, Nagendra Panini, Shanmuganathan, C., Shobana, M., Deepthi, Ch. Venkata Sasi, Bharathiraja, N. |
| Zdroj: |
International Journal of Communication Networks and Distributed Systems; 2023, Vol. 29 Issue: 5 p475-492, 18p |
| Abstrakt: |
Over the years, video compression has become a key method of sharing information and communicating in the world of media. The future image quality of the ISO standards groups will expect high quality images. Also, visual video codecs (VVC) have optimised encoding decisions that use the same average square error, or total square difference, for decades. At the same time, the sudden increase in deep learning (DL) methods raises the issue of whether DL could actually profoundly change the way the clip was programmed. We developed a new visual quality measurement (VQM) to find ways to make it better. The proposed approach could significantly reduce the encoding compute load while maintaining almost the same enhanced rate distortion optimisation (ERDO) efficiency as the previous encoder, based on experimental measurements. To further improve efficiency, the proposed methodology could be combined with fast motion search algorithms and filtration methods. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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