Deep Learning Based Video Compression

Ji, Kang Da; Hlavacs, Helmut

doi:10.1007/978-3-030-99188-3_8

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…The novelty of this research is to compare classical video compression algorithms with machine learning-based algorithms for the task of compressing video with minimal quality degradation. While classical video compression algorithms have been widely used for many years, machine learning algorithms are relatively new and still under development [10].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

Mochurad

2024

Technologies

View full text Add to dashboard Cite

show abstract

Deep Learning Based Video Compression

Cited by 1 publication

References 11 publications

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

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

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