Insights on Video Compression Strategies using Machine Learning
Veena S.K1, Mahesh K Rao2
1Veena S.K, Assistant Professor & Research Scholar, Department of ECE, MITM, Mysuru, Karnataka, India.
2Dr. Mahesh K Rao*, Professor & HOD, Department of ECE, MITM, Mysuru, Karnataka, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2724-2633 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9756109119/2019©BEIESP | DOI: 10.35940/ijeat.A9756.109119
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the rising advancement of the multimedia technology, video compression is becoming a challenging problem. Although, there is availability of various standard compression algorithms, yet robust compression performance is yet to be seen in existing compression techniques. This paper also highlights that machine learning plays a significant contributory role in improving the performance of the video compression. Therefore, this manuscript offers a technical insight about the performance of existing video compression technique using machine learning approach. The contribution of this paper is its findings which states that machine learning approach do have significant advantage but the advantageous features are limited by the inherent and unsolved research problem. The core findings of this paper are basically to highlight the strength and limitations of existing methods as well as to highlight the research gap in terms of open-end research problems which requires immediate attention.
Keywords: Video Compression, High Efficiency Video Coding, Machine Learning, Encoder, Decoder.