A Lossless Image Compression Technique using Simple Arithmetic Operations and its FPGA Implementation

Pattanaik, Sunil Kumar; Mahapatra, Kamalakanta

doi:10.1109/icit.2006.372532

Cited by 3 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The predictor model is limited by its reliance on image smoothness assumption and mandatory selection of binary or continuous-tone mode. In [25], a lossless digital image compression technique based on simple arithmetic operations and Xilinx Field Programmable Gate Array (FPGA) is proposed. The technique is most applicable for medical and scientific images where lossy compression is not practicable.…”

Section: Related Workmentioning

confidence: 99%

Lossless JPEG-Huffman Model for Digital Image Compression

Babatunde

2019

AIVP

View full text Add to dashboard Cite

The transmission of digital images has been bedevilled with limitation of storage and bandwidth capacities. One of the common strategies to resolving this limitation is to perform pre-transmission compression on the images. In this research, a lossless Joint Photography Expert Group (JPEG) and Huffman algorithms-based model for digital image compression is proposed. The lossless JPEG component of the model was used to perform Differential Pulse Coding Modulation (DPCM) on the pixels while adaptive Huffman coding was used for quality improvement and standardization. The implementation was carried out in an environment characterized by Windows 10 with Visual Basic as frontend on Personal Computer with 4 GB RAM, 500 GB ROM and 2.2 Ghz Core i3 Processor. The experimental images used for testing the algorithms were acquired from Signal and Image Processing Institute in the University of Southern California (USC-SIPI). Camera (Nikon D7000) and Geographical Information System (GIS) images were also used. Quantitative analyses of the experimental results and performance evaluation using Compression Ratio (CR), Bits per pixel (Bpp), Maximum Difference (MD), Mean Square Error (MSE), Root Mean Square Error (RMSE), Peak Signal Noise Ratio (PSNR), Average Difference (AD) and Structural Content (SC) were carried out. The analyses showed good compression rates and ratios for the proposed model. The superiority of the integrated model over some existing and related ones is also established.

show abstract

Section: Related Workmentioning

confidence: 99%

Lossless JPEG-Huffman Model for Digital Image Compression

Babatunde

2019

AIVP

View full text Add to dashboard Cite

show abstract

“…With the increasing use of multimedia technologies, image compression requires higher performance. To address needs and requirements of multimedia and internet applications, many efficient image compression techniques, with considerably different features, have been developed [1]. Traditionally, image compression adopts discrete cosine transform (DCT) in most situations which possess the characteristics of simpleness and practicality.…”

Section: Introductionmentioning

confidence: 99%