Deep Learning Based Video Compression Techniques with Future Research Issues

Joy, Helen K.; Kounte, Manjunath R; Chandrasekhar, Arunkumar; Paul, Manoranjan

doi:10.1007/s11277-023-10558-2

Cited by 7 publications

(1 citation statement)

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“…Machine learning-based video compression algorithms have shown promising results in recent research [14]. These algorithms utilize the power of deep learning models to compress video data without losing quality.…”

Section: Related Workmentioning

confidence: 99%

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

Mochurad

2024

Technologies

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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.

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Section: Related Workmentioning

confidence: 99%