scholarly journals Low Complexity near Lossless Image Compression Technique for Telemedicine

2011 ◽  
Vol 29 (7) ◽  
pp. 43-50 ◽  
Author(s):  
Mohit Gupta ◽  
Narendra D Iondhe
Keyword(s):  
Image Compression ◽  
Low Complexity ◽  
Compression Technique ◽  
Lossless Image Compression

Efficient Domain Search for Fractal Image Compression Using Feature Extraction Technique

2012 ◽  
Vol 488-489 ◽  
pp. 1587-1591
Author(s):  
Amol G. Baviskar ◽  
S. S. Pawale
Keyword(s):  
Feature Extraction ◽  
Image Compression ◽  
Low Complexity ◽  
Contractive Mapping ◽  
Fractal Image Compression ◽  
Compression Technique ◽  
Image Encoding ◽  
Fractal Image ◽  
Coding Method ◽  
The Given

Fractal image compression is a lossy compression technique developed in the early 1990s. It makes use of the local self-similarity property existing in an image and finds a contractive mapping affine transformation (fractal transform) T, such that the fixed point of T is close to the given image in a suitable metric. It has generated much interest due to its promise of high compression ratios with good decompression quality. Image encoding based on fractal block-coding method relies on assumption that image redundancy can be efficiently exploited through block-self transformability. It has shown promise in producing high fidelity, resolution independent images. The low complexity of decoding process also suggested use in real time applications. The high encoding time, in combination with patents on technology have unfortunately discouraged results. In this paper, we have proposed efficient domain search technique using feature extraction for the encoding of fractal image which reduces encoding-decoding time and proposed technique improves quality of compressed image.

scholarly journals Ideal Huffman Code for Lossless Image Compression for Ubiquitous Access

2018 ◽  
Vol 12 (2) ◽  
pp. 765 ◽  
Author(s):  
T Kavitha ◽  
K. Jayasankar
Keyword(s):  
Image Compression ◽  
Smart Phone ◽  
Computation Time ◽  
Digital Data ◽  
Compression Technique ◽  
Lossless Image Compression ◽  
Coding Efficiency ◽  
Average Computation Time ◽  
Ubiquitous Access ◽  
Cloud Storage Environment

<p>Compression technique is adopted to solve various big data problems such as storage and transmission. The growth of cloud computing and smart phone industries has led to generation of huge volume of digital data. Digital data can be in various forms as audio, video, images and documents. These digital data are generally compressed and stored in cloud storage environment. Efficient storing and retrieval mechanism of digital data by adopting good compression technique will result in reducing cost. The compression technique is composed of lossy and lossless compression technique. Here we consider Lossless image compression technique, minimizing the number of bits for encoding will aid in improving the coding efficiency and high compression. Fixed length coding cannot assure in minimizing bit length. In order to minimize the bits variable Length codes with prefix-free codes nature are preferred. However the existing compression model presented induce high computing overhead, to address this issue, this work presents an ideal and efficient modified Huffman technique that improves compression factor up to 33.44% for Bi-level images and 32.578% for Half-tone Images. The average computation time both encoding and decoding shows an improvement of 20.73% for Bi-level images and 28.71% for Half-tone images. The proposed work has achieved overall 2% increase in coding efficiency, reduced memory usage of 0.435% for Bi-level images and 0.19% for Half-tone Images. The overall result achieved shows that the proposed model can be adopted to support ubiquitous access to digital data.</p>

scholarly journals Image Compression with LZW (Lossless) On Different Image Formats

2018 ◽  
pp. 2153-2156
Author(s):  
Hitesh H Vandra
Keyword(s):  
Image Compression ◽  
Data Stream ◽  
Source Information ◽  
Compression Technique ◽  
Original Source ◽  
Lossless Image Compression ◽  
Source Data ◽  
Data Content ◽  
Lzw Algorithm ◽  
Compressed Data

Image compression is used to reduce bandwidth or storage requirement in image application. Mainly two types of image compression: lossy and lossless image compression. A Lossy Image Compression removes some of the source information content along with the redundancy. While the Lossless Image Compression technique the original source data is reconstructed from the compressed data by restoring the removed redundancy. The reconstructed data is an exact replica of the original source data. Many algorithms are present for lossless image compression like Huffman, rice coding, run length, LZW. LZW is referred to as a substitution or dictionary-based encoding algorithm. The algorithm builds a data dictionary of data occurring in an uncompressed data stream. Patterns of data (substrings) are identified in the data stream and are matched to entries in the dictionary. If the substring is not present in the dictionary, a code phrase is created based on the data content of the substring, and it is stored in the dictionary. The phrase is then written to the compressed output stream. In this paper we see the effect of LZW algorithm on the png, jpg, png, gif, bmp image formats.

Implementation of LOCO-I Lossless Image Compression Algorithm for Deep Space Applications

2014 ◽  
Vol 3 (3) ◽  
pp. 98
Author(s):  
P. Praveena
Keyword(s):  
Image Compression ◽  
Image Coding ◽  
Low Complexity ◽  
Vital Role ◽  
Space Science ◽  
Compression Algorithm ◽  
Deep Space ◽  
Space Applications ◽  
Lossless Image Compression ◽  
Image Compression Algorithm

<p>Present emerging trend in space science applications is to explore and utilize the deep space. Image coding in deep space communications play vital role in deep space missions. Lossless image compression has been recommended for space science exploration missions to retain the quality of image. On-board memory and bandwidth requirement is reduced by image compression. Programmable logic like field programmable gate array (FPGA) offers an attractive solution for performance and flexibility required by real time image compression algorithms. The powerful feature of FPGA is parallel processing which allows the data to process quicker than microprocessor implementation. This paper elaborates on implementing low complexity lossless image compression algorithm coder on FPGA with minimum utilization of onboard resources for deep space applications.</p>

A low complexity block-based adaptive lossless image compression

Optik ◽  
2013 ◽  
Vol 124 (24) ◽  
pp. 6545-6552 ◽  
Author(s):  
Long Yang ◽  
Xiaohai He ◽  
Gang Zhang ◽  
Linbo Qing ◽  
Tiben Che
Keyword(s):  
Image Compression ◽  
Low Complexity ◽  
Lossless Image Compression ◽  
Block Based

scholarly journals Lossless Image Compression Technique Using Combination Methods

2012 ◽  
Vol 05 (10) ◽  
pp. 752-763 ◽  
Author(s):  
A. Alarabeyyat ◽  
S. Al-Hashemi ◽  
T. Khdour ◽  
M. Hjouj Btoush ◽  
S. Bani-Ahmad ◽  
...  
Keyword(s):  
Image Compression ◽  
Compression Technique ◽  
Lossless Image Compression ◽  
Combination Methods
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