Hyperspectral image compression : adapting SPIHT and EZW to Anisotropic 3-D Wavelet Coding

Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Some wavelet-based compression algorithms have been successfully used for some hyperspectral space missions. This paper focuses on the optimization of a full wavelet compression system for hyperspectral images. Each step of the compression algorithm is studied and optimized. First, an algorithm to find the optimal 3-D wavelet decomposition in a rate-distortion sense is defined. Then, it is shown that a specific fixed decomposition has almost the same performance, while being more useful in terms of complexity issues. It is shown that this decomposition significantly improves the classical isotropic decomposition. One of the most useful properties of this fixed decomposition is that it allows the use of zero tree algorithms. Various tree structures, creating a relationship between coefficients, are compared. Two efficient compression methods based on zerotree coding (EZW and SPIHT) are adapted on this near-optimal decomposition with the best tree structure found. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images on six different areas presenting different statistical properties

Contemporary Affirmation of SPIHT Improvements in Image Coding

Set partitioning in hierarchal trees (SPIHT) is actually a widely-used compression algorithm for wavelet altered images. On most algorithms developed, SPIHT algorithm from the time its introduction in 1996 for image compression has got lots of interest. Though SPIHT is considerably simpler and efficient than several present compression methods since it's a completely inserted codec, provides good image quality, large PSNR, optimized for modern image transmission, efficient conjunction with error defense, form information on demand and hence element powerful error correction decreases from starting to finish but still it has some downsides that need to be taken away for its better use therefore since its development it has experienced many adjustments in its original model. This document presents a survey on several different improvements in SPIHT in certain fields as velocity, redundancy, quality, error resilience, sophistication, and compression ratio and memory requirement

RGB Medical Video Compression Using Geometric Wavelet

The video compression is used in a wide of applications from medical domain especially in telemedicine. Compared to the classical transforms, wavelet transform has significantly better performance in horizontal, vertical and diagonal directions. Therefore, this transform introduces high discontinuities in complex geometrics. However, to detect complex geometrics is one key challenge for the high efficient compression. In order to capture anisotropic regularity along various curves a new efficient and precise transform termed by bandelet basis, based on DWT, quadtree decomposition and optical flow is proposed in this paper. To encode significant coefficients we use efficient coder SPIHT. The experimental results show that the proposed algorithm DBT-SPIHT for low bit rate (0.3Mbps) is able to reduce up to 37.19% and 28.20% of the complex geometrics detection compared to the DWT-SPIHT and DCuT-SPIHT algorithm

Ultrafast and Efficient Scalable Image Compression Algorithm

Wavelet-based image compression algorithms have good performance and produce a rate scalable bitstream that can be decoded efficiently at several bit rates. Unfortunately, the discrete wavelet transform (DWT) has relatively high computational complexity. On the other hand, the discrete cosine transform (DCT) has low complexity and excellent compaction properties. Unfortunately, it is non-local, which necessitates implementing it as a block-based transform leading to the well-known blocking artifacts at the edges of the DCT blocks. This paper proposes a very fast and rate scalable algorithm that exploits the low complexity of DCT and the low complexity of the set partitioning technique used by the wavelet-based algorithms. Like JPEG, the proposed algorithm first transforms the image using block-based DCT. Then, it rearranges the DCT coefficients into a wavelet-like structure. Finally, the rearranged image is coded using a modified version of the SPECK algorithm, which is one of the best well-known wavelet-based algorithms. The modified SPECK consumes slightly less computer memory, has slightly lower complexity and slightly better performance than the original SPECK. The experimental results demonstrated that the proposed algorithm has competitive performance and high processing speed. Consequently, it has the best performance to complexity ratio among all the current rate scalable algorithms

Multiplicative Multiresolution Decomposition for Lossless Volumetric Medical Images Compression

With the emergence of medical imaging, the compression of volumetric medical images is essential. For this purpose, we propose a novel Multiplicative Multiresolution Decomposition (MMD) wavelet coding scheme for lossless compression of volumetric medical images. The MMD is used in speckle reduction technique but offers some proprieties which can be exploited in compression. Thus, as the wavelet transform the MMD provides a hierarchical representation and offers a possibility to realize lossless compression. We integrate in proposed scheme an inter slice filter based on wavelet transform and motion compensation to reduce data energy efficiently. We compare lossless results of classical wavelet coders such as 3D SPIHT and JP3D to the proposed scheme. This scheme incorporates MMD in lossless compression technique by applying MMD/wavelet or MMD transform to each slice, after inter slice filter is employed and the resulting sub-bands are coded by the 3D zero-tree algorithm SPIHT. Lossless experimental results show that the proposed scheme with the MMD can achieve lowest bit rates compared to 3D SPIHT and JP3D