A Comparison of Machine Learning-Based and Conventional Technologies for Video Compression

Abstract

The growing demand for high-quality video transmission over bandwidth-constrained networks and the increasing availability of video content have led to the need for efficient storage and distribution of large video files. To improve the latter, this article offers a comparison of six video compression methods without loss of quality. Particularly, H.255, VP9, AV1, convolutional neural network (CNN), recurrent neural network (RNN), and deep autoencoder (DAE). The proposed decision is to use a dataset of high-quality videos to implement and compare the performance of classical compression algorithms and algorithms based on machine learning. Evaluations of the compression efficiency and the quality of the received images were made on the basis of two metrics: PSNR and SSIM. This comparison revealed the strengths and weaknesses of each approach and provided insights into how machine learning algorithms can be optimized in future research. In general, it contributed to the development of more efficient and effective video compression algorithms that can be useful for a wide range of applications.

A Loss-initiated GAN-based Convolutional LSTM Method for Compression and Motion Estimation-Based Objective Enhancement in Images and Videos

“…The profound convolution neural network (CNN), which has led the neural network to resurge lately and has achieved remarkable success in both fake keen and sign handling domains, provides a fresh and encouraging option for video and image reduction [25,26]. Conventional video compression solutions employ predictive coding engineering to encapsulate residual and motion data [27]. In this paper, we leverage both the old-fashioned design of the traditional video compression method [28] and the solid non-straight representation capabilities of neural networks to come up with the first full video compression deep model that enhances all the elements of video compression at the same time.…”