International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 5, No. 3, 8 Sep. 2015
Cover page and Table of Contents: PDF (size: 430KB)
A Review: DWT-DCT Technique and Arithmetic-Huffman Coding based Image Compression
Full Text (PDF, 430KB), PP.20-33
Views: 0 Downloads: 0
Author(s)
Gaurav Kumar
1,*
Er. Sukhreet Singh Brar
2
Rajeev Kumar
1
Ashok Kumar
1
Index Terms
DWT, DCT, Quantization, Arithmetic Coding, Huffman Coding, PSNR, CR
Abstract
Nowadays, the volume of the data is increasing with time which generates a problem in storage and transfer. To overcome this problem, the data compression is the only solution. Data compression is the science (or an art) of representing information in compact form. This is an active research area. Compression is to save the hardware storage space and transmission bandwidth by reducing the redundant bits. Basically, lossless & lossy are two types of data compression technique. In lossless data compression, original data is similar to decompressed or decoded data, but in lossy technique is not same. In this paper, Study lossless image compression technique. The purpose of image compression is to maximum bandwidth utilization and reduces storage capacity. This technique is beneficial to image storage and transfer. At the present time, Mostly image compression research have focused on the wavelet transform due to better performance over another transform. The performance is evaluated by using MSE & PSNR. DWT, quantization, Arithmetic, Huffman coding and DCT techniques are briefly introduced. After decompression, the quality of image is evaluated using PSNR parameter between original & decoded image. Compression ratio (CR) parameter is calculated to measure how many times image compressed.
Cite This Paper
Gaurav Kumar, Er. Sukhreet Singh Brar, Rajeev Kumar, Ashok Kumar,"A Review: DWT-DCT Technique and Arithmetic-Huffman Coding based Image Compression", IJEM, vol.5, no.3, pp.20-33, 2015. DOI: 10.5815/ijem.2015.03.03
Reference
[1]Gonzalez, R. C., & Woods, R. E. (2002). Digital Image Processing. New Jersey: Pearson Prentice Hall.
[2]Sayood, K. (2006). Introduction to Data Compression. San Francisco: Morgan Kaufmann. Roy, V. (2013).
[3]Mahfouz, A. A., & Salem, F. A. (2013). Performance Analysis, Controller Selection And Verification Of Electric Motor For Mechatronics Motion Control Applications, Using New MATLAB Built-In Function And Simulink Model. International Journal of Engineering and Manufacturing (IJEM), 3(2), 11.
[4]Spatial and Transform Domain Filtering Method for Image De-noising: A Review. International Journal of Modern Education and Computer Science (IJMECS), 5(7), 41.
[5]Dorairangaswamy, M. A & Padhmavathi, B. (2009). An effective blind watermarking scheme for protecting rightful ownership of digital images. In TENCON 2009 IEEE Region 10 Conference,1-6.
[6]Corinthios, Benchikh, S & Michael. (2011). A Hybrid Image Compression Technique Based On DWT and DCT Transforms, 1049-1065. Canada: IEEE.
[7]Gupta, M & Garg, A. K. (2012). Analysis of image compression algorithm using DCT. International Journal of Engineering Research and Applications (IJERA), 2(1): 515-521.
[8]Marpe, D., Schwarz, H & Wiegand, T. (2003). Context-based adaptive binary arithmetic coding in the H. 264/AVC video compression standard. Circuits and Systems for Video Technology, IEEE Transactions on, 13(7): 620-636.
[9]Venkatasekhar, D., & Aruna, P. (2013). A Fast Fractal Image Compression Using Huffman Coding. Asian Journal of Computer Science & Information Technology, 2(9): 272 – 275.
[10]Maan, A. J.(2013). Analysis and Comparison of Algorithms for Lossless Data Compression. International Journal of Information and Computation Technology, ISSN, 0974-2239, 3:139-146.
[11]Abouali, A. H. (2015). Object-based VQ for image compression. Ain Shams Engineering Journal, 6: 211-216.
[12]Howard, P. G & Vitter, J. S. (1996). Parallel lossless image compression using Huffman and arithmetic coding. Information processing letters, 59(2): 65-73.
[13]Gershikov, E., Lavi-Burlak, E & Porat, M. (2007). Correlation-based approach to color image compression. Signal Processing: Image Communication, 22(9): 719-733.
[14]Chen, Y. Y. (2007). Medical image compression using DCT-based subband decomposition and modified SPIHT data organization. International journal of medical informatics, 76(10): 717-725.
[15]Chang, C. C., Lin, C. C., Tseng, C. S & Tai, W. L. (2007). Reversible hiding in DCT-based compressed images. Information Sciences, 177(13): 2768-2786.
[16]Khorrami, H & Moavenian, M. (2010). A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert systems with Applications, 37(8): 5751-5757.
[17]Mohammed, A. A & Hussein, J. A. (2010, December). Hybrid transform coding scheme for medical image application. 2010 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 237-240.
[18]Corinthios, Benchikh, S & Michael. (2011). A Hybrid Image Compression Technique Based On DWT and DCT Transforms, 1049-1065. Canada: IEEE.
[19]Chowdhury, M. M. H & Khatun, A. (2012). Image Compression Using Discrete Wavelet Transform. IJCSI International Journal of Computer Science Issues, 9(4): 327-330.
[20]Bindu, K., Ganpati, A & Sharma, A. K. (2012). A Ccomparative Study of Image Compression Algorithm. International Journal of Research in Computer Science, 2(5): 37-42.
[21]Bharath, K. N., Padmajadevi, G. & Kiran. (2013). Hybrid Compression Using DWT-DCT and Huffman Encoding Techniques for Biomedical image and video application. International Journal of Computer Science and Mobile Computing, 2(5): 255-261.
[22]Hazarathaiah, A., Rao, P & Madhu, C. (2013). Medical Image Compression Using SPIHT Combined With Arithmetic. International Journal of Electronics and Communication Engineering, 2(5): 1-6.
[23]Jafari, R., Ziou, D & Rashidi, M. M. (2013). Increasing image compression rate using steganography. Expert Systems with Applications, 40(17): 6918-6927.
[24]Rehman, M., Sharif, M & Raza, M. (2014). Image compression: A survey. Research Journal of Applied Sciences, Engineering and Technology, 7(4): 656-672.
[25]Wu, M. S. (2014). Genetic algorithm based on discrete wavelet transformation for fractal image compression. Journal of Visual Communication and Image Representation, 25(8): 1835-1841.
[26]Vijayabhaskar, P. V. M & Raajan, N. R. (2013). Comparison of wavelet filters in image coding using hybrid compression technique. 2013 International Conference on Emerging Trends in VLSI, Embedded System, Nano Electronics and Telecommunication System (ICEVENT), 1-5.
[27]Starosolski, R. (2014). New simple and efficient color space transformations for lossless image compression. Journal of Visual Communication and Image Representation, 25(5): 1056-1063.