Survey of Color Image Compression using Block Partition and DWT Technique
DOI:
https://doi.org/10.26438/ijcse/v7i6.230234%20Keywords:
Discrete Wavelet Transform, Multi-level, Block Truncation Code (BTC), PSNR MSE, Compression RatioAbstract
In the present era of multimedia, the requirement of image/video storage and transmission for video conferencing, image and video retrieval, video playback, etc. are increasing exponentially. As a result, the need for better compression technology is always in demand. Modern applications, in addition to high compression ratio, also demand for efficient encoding and decoding processes, so that computational constraint of many real-time applications is satisfied. Two widely used spatial domain compression techniques are discrete wavelet transform and multi-level block truncation coding (BTC). DWT method is used to stationary and non-stationary images and applied to all average pixel value of image. Muli-level BTC is a type of lossy image compression technique for greyscale images. It divides the original images into blocks and then uses a quantizer to reduce the number of grey levels in each block whilst maintaining the same mean and standard deviation. In this paper is studied of Multi-level BTC and DWT technique for for gray and color image.
References
[1] Shuyuan Zhu, Zhiying He, Xiandong Meng, Jiantao Zhou and Bing Zeng, “Compression-dependent Transform Domain Downward Conversion for Block-based Image Coding”, IEEE Transactions on Image Processing, Volume: 27, Issue: 6, June 2018.
[2] Julio Cesar Stacchini de Souza, Tatiana Mariano Lessa Assis, and Bikash Chandra Pal, “Data Compression in Smart Distribution Systems via Singular Value Decomposition”, IEEE Transactions on Smart Grid, Vol. 8, NO. 1, January 2017.
[3] Sunwoong Kim and Hyuk-Jae Lee, “RGBW Image Compression by Low-Complexity Adaptive Multi-Level Block Truncation Coding”, IEEE Transactions on Consumer Electronics, Vol. 62, No. 4, November 2016.
[4] C. Senthil kumar, “Color and Multispectral Image Compression using Enhanced Block Truncation Coding [E-BTC] Scheme”, accepted to be presented at the IEEE WiSPNET, PP. 01-06, 2016 IEEE.
[5] Jing-Ming Guo, Senior Member, IEEE, and Yun-Fu Liu, Member, IEEE, “Improved Block Truncation Coding Using Optimized Dot Diffusion”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 23, NO. 3, MARCH 2014.
[6] Jayamol Mathews, Madhu S. Nair, “Modified BTC Algorithm for Gray Scale Images using max-min Quantizer”, 978-1-4673-5090-7/13/$31.00 ©2013 IEEE.
[7] Ki-Won Oh and Kang-Sun Choi, “Parallel Implementation of Hybrid Vector Quantizerbased Block Truncation Coding for Mobile Display Stream Compression”, IEEE ISCE 2014 1569954165.
[8] Seddeq E. Ghrare and Ahmed R. Khobaiz, “Digital Image Compression using Block Truncation
Coding and Walsh Hadamard Transform Hybrid Technique”, 2014 IEEE 2014 International Conference on Computer, Communication, and Control Technology (I4CT 2014), September 2 - 4, 2014 - Langkawi, Kedah, Malaysia.
[9] M. Brunig and W. Niehsen. Fast full search block matching. IEEE Transactions on Circuits and Systems for Video Technology, 11:241 – 247, 2001.
[10] K. W. Chan and K. L. Chan. Optimisation of multi-level block truncation coding. Signal Processing: Image Communication, 16:445 – 459, 2001.
[11] C. C. Chang and T. S. Chen. New tree-structured vector quantization with closed-coupled multipath searching method. Optical Engineering, 36:1713 – 1720, 1997.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
