Er. Sukhreet Singh Brar

Work place: Centre for Computer Science and Technology Central university of Punjab, Bathinda, India



Research Interests: Computer systems and computational processes, Image Compression, Image Manipulation, Image Processing


Sukhreet Singh Brar received the B.Tech degree in Electronics & Communication Engineering from Punjab Technical University, Jalandhar in the year 2008 and M.Tech. Degree in Electronics and Communication Engineering from Punjabi University Patiala, 2011. Currently working as Assistant professor in Centre for Computer Science and Technology, Central university of Punjab, Bathinda, India. His research area interest is in the area of image and video compression.

Author Articles
A Review: DWT-DCT Technique and Arithmetic-Huffman Coding based Image Compression

By Gaurav Kumar Er. Sukhreet Singh Brar Rajeev Kumar Ashok Kumar

DOI:, Pub. Date: 8 Sep. 2015

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.

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