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Journal of Artificial Intelligence and Capsule Networks

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Home / Archives / Volume-2 / Issue-2 / Article-6

Volume - 2 | Issue - 2 | june 2020

Video Compression for Surveillance Application using Deep Neural Network
Prasanga Dhungel, Prashant Tandan, Sandesh Bhusal, Sobit Neupane, Subarna Shakya  256  175
Pages: 131-145
Cite this article
Dhungel, P., Tandan, P., Bhusal, S., Neupane, S. & Shakya, S. (2020). Video Compression for Surveillance Application using Deep Neural Network. Journal of Artificial Intelligence and Capsule Networks, 2(2), 131-145. doi:10.36548/jaicn.2020.2.006
Published
03 June, 2020
Abstract

We present a new approach to video compression for video surveillance by refining the shortcomings of conventional approach and substitute each traditional component with their neural network counterpart. Our proposed work consists of motion estimation, compression and compensation and residue compression, learned end-to-end to minimize the rate-distortion trade off. The whole model is jointly optimized using a single loss function. Our work is based on a standard method to exploit the spatio-temporal redundancy in video frames to reduce the bit rate along with the minimization of distortions in decoded frames. We implement a neural network version of conventional video compression approach and encode the redundant frames with lower number of bits. Although, our approach is more concerned toward surveillance, it can be extended easily to general purpose videos too. Experiments show that our technique is efficient and outperforms standard MPEG encoding at comparable bitrates while preserving the visual quality.

Keywords

Video compression Motion Estimation Auto-encoder Rate-Distortion minimization Bitrate Estimation

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