Subject Review: Video Compression Algorithms
Review Article
DOI:
https://doi.org/10.31695/IJERAT.2020.3668Keywords:
Video compression, PSNR, Frame, Spatial redundancy, Discrete Cosine Transform.Abstract
Video compression is the process of decreasing the number of bits required to represent a certain video. Video compression can be done by a specific algorithm for deciding the most ideal approach to reduce the amount of data storage. The video file is coded in such a way that consuming less space than the original file and is easier to transmit over the Internet. The basic idea of video compression based on removing the redundant data that exists in the video. There are four types of redundancy in digital video: colorize redundancy, temporal redundancy, statistical redundancy, and spatial redundancy. Video compression algorithms must reduce these redundancies in such a way that keep the quality of the compressed video when the decompression process is done. Most video compression techniques consist of the following steps: Motion Estimation,Motion Compensation, Discrete Cosine Transform, Run Length Encoding, Huffman Coding. Frame Difference. This paper discusses the characteristic of video coding from the scratch of key frame selection to the evolutions of various standards
References
Hussain,A. & Ahmed,Z .(2018). A Survey on Video Compression Fast Block
Matching Algorithms, Neurocomputing , doi: https://doi.org/10.1016/j.neucom.2018.10.060
BOVIK, A. C. (2009). "Chapter 1 - Introduction to Digital Video Processing". In:The Essential Guide to Video Processing, 2nd edition; Boston: Academic Press.
Bahari, A., Arslan, T., & Erdogan, A. T. (2010). Interframe Bus Encoding Technique and Architecture for MPEG4 AVC/H.264 Video Compression, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, DOI: 10.1109/TVLSI.2009.2015324.
Abedi, M.; Sun, B.; Zheng, Z. (July 2019). "A Sinusoidal-Hyperbolic Family of Transforms With Potential Applications in Compressive Sensing". IEEE Transactions on Image Processing. 28 (7): 3571–3583.
Abedi, M., Sun, B. & Zheng , Z. (July 2019). A Sinusoidal-Hyperbolic Family of Transforms With Potential Applications in Compressive Sensing. IEEE Transactions on Image Processing. 28 (7): 3571–3583
Haseeb, S. & O.O. Khalifa,( 2006). Comparative performance analysis of image compression by JPEG 2000: A case study on medical images. Inform. Technol. J., 5: 35-39.
Mitchell,J.L. , Pennebaker,W.,B., Fogg,C.,E. & LeGall,D.,J,(1996), “MPEG Video Compression Standard,” Digital Multimedia Standards Series, Chapman and Hall, London, pp. 135–169.
Rajwant, K. & Anshu, B.(2018), A Recent Survey of Different Video Compression Methods, International Journal of IT & Knowledge Management, Vol. 11 , NO. 2 pp. 103-109
Nilsson, M. & M. Naylor,( 2003). Comparison of H.263 and H.26L video compression performance with web-cams. Electronics Lett., 39: 277-278.
Iain E. G. (2002), Video Coding Standards: H.261, H.263 and H.26L, John Wiley & Sons.
SULLIVAN, G., TOPIWALA, P. & LUTHRA, A. (2004). "The H.264/AVC Advanced Video Coding Standard: Overview and Introduction to the Fidelity Range Extensions". SPIE conference on Applications of Digital Image Processing XXVII.
Miroslav, U., Jaroslav,F. , Lukas, S. & Martin ,V.(2014), Impact of H.264/AVC and H.265/HEVC Compression Standards on the Video Quality for 4K resolution, DIGITAL IMAGE PROCESSING AND COMPUTER GRAPHICS,vol. 12 ,No.14
Downloads
Published
Issue
Section
License
Copyright (c) 2020 Amal Abbas Kadhim, Azal Minshed Abid, Zuhair Hussein Ali
This work is licensed under a Creative Commons Attribution 4.0 International License.
