An Image Compression Scheme using SMVQ and Modified Search-Order-Coding Technique

2011 ◽  
Vol 2 (3) ◽  
pp. 86-95
Author(s):  
ShihChieh Shie LongTai Chen
Keyword(s):  
Image Compression ◽  
Compression Scheme ◽  
Search Order

FPGA implementation of a near computation free image compression scheme based on adaptive decimation

Integration ◽  
2003 ◽  
Vol 36 (3) ◽  
pp. 121-143 ◽  
Author(s):  
Angus Wu ◽  
P.W.M. Tsang ◽  
Johnson Tang
Keyword(s):  
Image Compression ◽  
Fpga Implementation ◽  
Compression Scheme

scholarly journals Improving Fractal Image Compression Scheme through Quantization Operation

2007 ◽  
Vol 4 (2) ◽  
pp. 330-337
Author(s):  
Baghdad Science Journal
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
New Method ◽  
Compression Scheme ◽  
Fractal Image Compression ◽  
High Image Quality ◽  
Fractal Image ◽  
Separate Step ◽  
Transform Coefficients

We explore the transform coefficients of fractal and exploit new method to improve the compression capabilities of these schemes. In most of the standard encoder/ decoder systems the quantization/ de-quantization managed as a separate step, here we introduce new way (method) to work (managed) simultaneously. Additional compression is achieved by this method with high image quality as you will see later.

Low-complexity and energy efficient image compression scheme for wireless sensor networks

2008 ◽  
Vol 52 (13) ◽  
pp. 2594-2603 ◽  
Author(s):  
Qin Lu ◽  
Wusheng Luo ◽  
Jidong Wang ◽  
Bo Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Image Compression ◽  
Energy Efficient ◽  
Low Complexity ◽  
Wireless Sensor ◽  
Compression Scheme

A Hybrid Lossless-Lossy Binary Image Compression Scheme

2013 ◽  
Vol 3 (4) ◽  
pp. 37-50
Author(s):  
Saif alZahir ◽  
Syed M. Naqvi
Keyword(s):  
Image Compression ◽  
Mathematical Models ◽  
Regression Models ◽  
Input Image ◽  
Binary Image ◽  
Large Set ◽  
Image Simulation ◽  
Compression Scheme ◽  
Binary Images ◽  
Visual Distortion

In this paper, the authors present a binary image compression scheme that can be used either for lossless or lossy compression requirements. This scheme contains five new contributions. The lossless component of the scheme partitions the input image into a number of non-overlapping rectangles using a new line-by-line method. The upper-left and the lower-right vertices of each rectangle are identified and the coordinates of which are efficiently encoded using three methods of representation and compression. The lossy component, on the other hand, provides higher compression through two techniques. 1) It reduces the number of rectangles from the input image using our mathematical regression models. These mathematical models guarantees image quality so that rectangular reduction should not produce visual distortion in the image. The mathematical models have been obtained through subjective tests and regression analysis on a large set of binary images. 2) Further compression gain is achieved through discarding isolated pixels and 1-pixel rectangles from the image. Simulation results show that the proposed schemes provide significant improvements over previously published work for both the lossy and the lossless components.

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