A New Approach in Digital Image Compression Using Unequal Error Protection (UEP)

2014 ◽  
Vol 704 ◽  
pp. 403-407
Author(s):  
Okuwobi Idowu Paul ◽  
Yong Hua Lu

This paper proposes a new algorithms for compression of digital images especially at the encoding stage of compressive sensing. The research consider the fact that a certain region of a given imagery is more important in most applications. The first algorithm proposed for the encoding stage of Compressive Sensing (CS) exploits the known structure of transform image coefficients. The proposed algorithm makes use of the unequal error protection (UEP) principle, which is widely used in the area of error control coding. The second algorithm which exploits the UEP principle to recover the more important part of an image with more quality while the rest part of the image is not significantly degraded. The proposed algorithm shown to be successful in digital image compression where images are represented in the spatial and transform domains. This new algorithm were recommended for use in image compression.

Author(s):  
Kandarpa Kumar Sarma

The explosive growths in data exchanges have necessitated the development of new methods of image compression including use of learning based techniques. The learning based systems aids proper compression and retrieval of the image segments. Learning systems like. Artificial Neural Networks (ANN) have established their efficiency and reliability in achieving image compression. In this work, two approaches to use ANNs in Feed Forward (FF) form and another based on Self Organizing Feature Map (SOFM) is proposed for digital image compression. The image to be compressed is first decomposed into smaller blocks and passed to FFANN and SOFM networks for generation of codebooks. The compressed images are reconstructed using a composite block formed by a FFANN and a Discrete Cosine Transform (DCT) based compression-decompression system. Mean Square Error (MSE), Compression ratio (CR) and Peak Signal-to-Noise Ratio (PSNR) are used to evaluate the performance of the system.


1999 ◽  
Vol 5 (6) ◽  
pp. 379-383 ◽  
Author(s):  
Cheng Yimin ◽  
Wang Yixiao ◽  
Sun Qibin ◽  
Sun Longxiang

1998 ◽  
Vol 15 (4) ◽  
pp. 363-368
Author(s):  
DEREK A. FYFE ◽  
FRED EMGE ◽  
GREGORY JOHNSON ◽  
FRANCIS McCAFFREY ◽  
WILLIAM LUTEN ◽  
...  

Author(s):  
Yu-Chen Hu ◽  
Chin-Chen Chang

In this paper, a new edge detection scheme based on block truncation coding (BTC) is proposed. As we know, the BTC is a simple and fast scheme for digital image compression. To detect an edge boundary using the BTC scheme, the bit plane information of each BTC-compressed block is exploited, and a simple block type classifier is introduced. The experimental results show that the proposed scheme clearly detects the edge boundaries of digital images while requiring very little computational complexity. Meanwhile, the edge detection process can be incorporated into all BTC variant schemes. In other words, the newly proposed scheme provides a good approach for the detection of edge boundaries using block truncation coding.


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