Authors:
Zahra Montajabi
;
Vahid Ghassab
and
Nizar Bouguila
Affiliation:
Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada
Keyword(s):
Computer Vision, Deep Neural Networks, Invertible Neural Network (INN), Video Coding.
Abstract:
Due to the recent advent of high-resolution mobile and camera devices, it is necessary to develop an optimal solution for saving the new video content instead of traditional compression methods. Recently, video compression received enormous attention among computer vision problems in media technologies. Using state-of-the-art video compression methods, videos can be transmitted in a better quality requiring less band-width and memory. The advent of neural network-based video compression methods remarkably promoted video coding performance. In this paper, an Invertible Neural Network (INN) is utilized to reduce the information loss problem. Unlike the classic auto-encoders which lose some information during encoding, INN can preserve more information and therefore, reconstruct videos with more clear details. Moreover, they don’t increase the complexity of the network compared to traditional auto-encoders. The proposed method is evaluated on a public dataset and the experimental result
s show that the proposed method outperforms existing standard video encoding schemes such as H.264 and H.265 in terms of peak signal-to-noise ratio (PSNR), video multimethod assessment fusion (VMAF), and structural similarity index measure (SSIM).
(More)