Paper Abstract and Keywords |
Presentation |
2018-10-19 09:30
Perceptual Quality Driven Adaptive Video Coding for VOD Streaming Yusuke Sakamoto, Masaru Takeuchi, Shintaro Saika, Tatsuya Nagashima, Zhengxue Cheng, Kenji Kanai, Jiro Katto (Waseda Univ.), Kaijin Wei, Ju Zengwei, Xu Wei (Huawei Technologies) IE2018-44 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In video streaming on the Internet, getting a good encoding recipe (i.e. bitrate-resolution pairs) is a main problem to deliver the highest quality video streams. By using multiple bitrate/resolution encoding like MPEG-DASH, video streaming services can deliver the best video stream to a client, under the conditions of the client’s available bandwidth and viewing device capability. However, conventional fixed encoding recipes (i.e. resolution-bitrate pairs) suffer from many problems, such as improper resolution selection and stream redundancy. To avoid these problems, we propose a novel video coding method, which generates multiple representations with constant Just-Noticeable Difference (JND) interval, using a JND scale estimator which we developed with Support Vector Regression (SVR). Experimental results confirm that our proposed method can achieve better rate-JND characteristics than conventional fixed recipe. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Adaptive Video Coding / Just-Noticeable Difference / Perceptual Video Quality / Machine Learning / MPEG-DASH / / / |
Reference Info. |
IEICE Tech. Rep., vol. 118, no. 259, IE2018-44, pp. 1-6, Oct. 2018. |
Paper # |
IE2018-44 |
Date of Issue |
2018-10-12 (IE) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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IE2018-44 |
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