Modification of the LSB Implementation Method of Digital Watermarks

Havrysh, Bohdana; Tymchenko, Oleksandr; Izonin, Ivan

doi:10.1007/978-3-031-04809-8_9

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of the data generated in the modern world are video [1,2]. The task of video compression has become an integral part of modern multimedia communication systems [3].…”

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

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.

show abstract

“…Most of the data generated in the modern world are video [1,2]. The task of video compression has become an integral part of modern multimedia communication systems [3].…”

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

Modification of the LSB Implementation Method of Digital Watermarks

Cited by 1 publication

References 21 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

Contact Info

Product

Resources

About