American Journal of Applied Sciences

Still Image Compression Using Texture and Non Texture Prediction Model

G. Mohanbaabu and P. Renuga

DOI : 10.3844/ajassp.2012.519.525

American Journal of Applied Sciences

Volume 9, Issue 4

Pages 519-525


Problem statement: Existing lossless image compression schemes attempt to do prediction in an image data using their Local Binary Pattern (LBP) and their spatial neighborhood based techniques. In the previous techniques such as Vector Quantization (VQ) and Gradient Adjusted Prediction (GAP) the texture and non-texture regions are not classified separately. Texture and Non-texture images prophecy has been a key factor in efficient lossless image compression. Hence, there is a need to develop a more efficient image prediction scheme to exploit these texture components. Approach: In this research, an efficient visual quality technique for image compression is proposed. The image is classified into texture and non-texture regions by using an Artificial Neural Network (ANN) Classifier. The texture region is encoded with the Similar Block Matching (SBM) encoder and the non-texture region is encoded with SPIHT encoding. Results: The proposed texture prediction based compression is compared with the existing compression techniques such as H.264 and JPEG. From the result it reveals that the Peak Signal to Noise Ratio (PSNR) values of all the test images is higher in the proposed technique as compared to JPEG technique. Similarly PSNR values are low in H.264 for all the images except Boat image when compared to the proposed technique. This result concludes that the increase in PSNR indicates that the output image has less noise as compared to existing techniques. Conclusion: The compression of the proposed algorithm is superior to JPEG and H.264. Our new method of compression algorithm can be used to improve the performance of Compression ratio and Peak Signal to Noise Ratio (PSNR). In future this study can be extended to real time applications for video compression in medical images.


© 2012 G. Mohanbaabu and P. Renuga. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.