Perceptual Quality Driven Adaptive Video Coding Using JND Estimation

Masaru Takeuchi, Shintaro Saika, Yusuke Sakamoto, Tatsuya Nagashima, Zhengxue Cheng, Kenji Kanai, Jiro Katto, Kaijin Wei, Ju Zengwei, Xu Wei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


We introduce a perceptual video quality driven video encoding solution for optimized adaptive streaming. 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 JustNoticeable Difference (JND) interval. For this purpose, we developed a JND scale estimator using Support Vector Regression (SVR), and designed a pre-encoder which outputs an encoding recipe with constant JND interval in an adaptive manner to input video.

Original languageEnglish
Title of host publication2018 Picture Coding Symposium, PCS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)9781538641606
Publication statusPublished - 2018 Sep 5
Event33rd Picture Coding Symposium, PCS 2018 - San Francisco, United States
Duration: 2018 Jun 242018 Jun 27


Other33rd Picture Coding Symposium, PCS 2018
CountryUnited States
CitySan Francisco

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology

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