High Efficient Quality of Video Compression using Variational Autoencoders in Deep Learning

The past few years researchers are rising dominance of deep learning and artificial intelligence in huge range of various applications like image and video compression. The paper provides a deep concept over for highly loss video compression model. In the existing neural networks of video encoding presence of quantity and level of redundancy provides unique problems. The ELBO utilized in variational autoencoders is strongly related to the rate distortion loss, which the autoencoder and prior are jointly trained to reduce. Although proposed method is straightforward, in this paper discover that it beats cutting-edge learning video compression networks interpolation. The work where systematically assess key design decisions, including the type of autoregressive prior and the usage of frame-based or spatio-temporal autoencoders. And also provide three modifications to the fundamental concept that highlight its advantages over more traditional compression techniques. In this paper proposed semantic compression, which trains the model to devote more bits to important objects. This work investigates the proposed multimodal compression, where it will presents how well our approach works when it comes to combining different modalities that have been collected by unconventional imaging sensors, such quad cameras. And finalize that approach makes unique video compression applications possible that were not possible with conventional codecs. Finally, at the end of the work in our multimodal compression studies significantly improves compression performance with the PSNR of 48. For future scope of work, a simple decoder can recreate videos with sufficient quality without compromising classification accuracy.