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, Zengwei Ju, Wei Xu · 2018
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 Just-Noticeable 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.