Stereoscopic Image Compression using Disparity Estimation between Images and 2D Digital Curvelet Transformation

Image compression is one of the most important branches of digital image processing. It reduces the size of the captured images and minimizes the storage space on the drivers to speed up the transferring and transmission. In this paper we will present a new approach for compressing stereo images based on three algorithms; the first one is comparing the two images that perform the stereoscopic view by noticing the great similarities between them and encoding the difference between the two images instead of encoding the whole image. The second one is reducing the redundancy between the Pixels using a 2D Digital Curvelet Transformation so we can utilize the great ability to represent the curves in the image with minimum number of coefficients. Then quantize them and remove undesirable coefficient. The low number of coefficient contains most of image data. Last one is using Huffman Encoding and take advantage of the lossless property so we can encode image and reduce the size of data without getting any image distortion or lose any part of this image. The performance of the proposed algorithm evaluated using Compression Ratio standard which is the number of the image bits after compression to the number of the original image bits before compression. Also, Peak Signal to Noise Ratio standard (PSNR) which represent the similarity between the restored image and the original image. In final, the Mean Square Error standard (MSE) which represent the error between the restored image and original image. In conclusion, the main objective here is to get the lowest rate for image compression ratio with the highest value for the image quality PSNR at the lowest value of the errors MSE.

Fractal Compression of Stereo Images Using Block Matching and SGM Algorithms to Obtain the Disparity Map

Considering the increasing importance of stereo image compression and Fractal geometry becoming one of the most important fields of modern science ,we applied fractal image compression based on quadtree portioning method and global search algorithm , on a group of stereo image pairs . As the stereo image consists of two planar images , left and right .Both the left image ( reference image ) and the disparity map between left and right images , were compressed using fractal compression . We applied both block matching algorithm and Semi Global Method (SGM)to obtain the disparity map. The left image and the depth map were reconstructed using fractal decompression while the right image (target image ) was reconstructed using the reconstructed left image , disparity map and the error image between the original right image and the reconstructed right image that was build from the left image and the disparity map . The results were evaluated using quality objective measures which are MSE (Mean Square Error ) and PSNR (Peak Signal to Noise Ratio) and efficiency objective measures which are CR(Compression Ratio) and compression time . The results were compared with JPEG compression of stereo pairs based on Discrete Cosine Transform DCT and JPEG2000 compression of stereo pairs on stereo image based on Discrete Wavelet Transform DWT .