Image Compression: A Survey <s> EFFICIENT COMPRESSION ALGORITHM FOR HYPER SPECTRAL IMAGES <s> Quadtree decomposition is a simple technique used to obtain an image representation at different resolution levels. This representation can be useful for a variety of image processing and image compression algorithms. This paper presents a simple way to get better compression performances (in MSE sense) via quadtree decomposition, by using near to optimal choice of the threshold for quadtree decomposition; and bit allocation procedure based on the equations derived from rate-distortion theory. The rate-distortion performance of the improved algorithm is calculated for some Gaussian field, and it is examined vie simulation over benchmark gray-level images. In both these cases, significant improvement in the compression performances is shown. <s> BIB001 </s> Image Compression: A Survey <s> EFFICIENT COMPRESSION ALGORITHM FOR HYPER SPECTRAL IMAGES <s> Due to significantly improved spectral resolution produced by hyperspectral sensors, the band-to-band correlation is generally very high and can be removed without loss of crucial information. Data compression is an effective means to eliminate such redundancy resulting from high interband correlation. In hyperspectral imagery, various information comes from different signal sources, which include man-made targets, natural backgrounds, unknown clutters, interferers, unidentified anomalies, etc. In many applications, whether or not a compression technique is effective is measured by the degree of information loss rather than information recovery. For example, compression of noise or interferers is highly desirable to image analysis and interpretation. In this paper, we present an unsupervised fully constrained least squares (UFCLS) linear spectral mixture analysis (LSMA)-based compression technique for hyperspectral target detection and classification. Unlike most compression techniques, which deal directly with grayscale images, the proposed compression approach generates and encodes the fractional abundance images of targets of interest present in an image scene to achieve data compression. Since the vital information used for image analysis is generally preserved and retained in these fractional abundance images, the loss of information may have little impact on image analysis. On some occasions, it even improves performance analysis. Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) and Hyperspectral Digital Imagery Collection Experiment (HYDICE) data are used for experiments to evaluate our proposed LSMA-based compression technique used for applications in hyperspectral detection and image classification. The classification results using the original data and the UFCLS-decompressed data are shown to be very close with no visible difference. But a compression ratio for the HYDICE data with water bands removed can achieve as high as 138:1 with peak SNR (PSNR) 33 dB, while a compression ratio of the AVIRIS scene also with water bands removed is 90:1 with PSNR 40 dB. <s> BIB002
The proper A3D-DWT and asymmetric zero tree are effectively merged together for rate-distortion improvement in proposed lossy method of compression. It is shown by experiments that the proposed adaptive AT3DSPIHT method performs not only much good then the existing compression schemes relay on 3D-DWT and A3D-DWT for hyper spectral images but also better perform at the weakest correlated spectral bands then 2DSPIHT BIB001 . For analyzing image which are hyper spectral in which for compression criteria target detection and classification were used. With an unsupervised learning first we identified interest target in a hyper spectral image scene. To obtain fractional abundance images the entire image cube is compressed for these targets BIB002 .