Lossy to lossless object-based coding of 3-D MRI data

We propose a fully three-dimensional object-based coding system exploiting the diagnostic relevance of the different regions of the volumetric data for rate allocation. The data are first decorrelated via a 3D discrete wavelet transform. The implementation via the lifting steps scheme allows to map integer-to-integer values, enabling lossless coding, and facilitates the definition of the object-based inverse transform. The coding process assigns disjoint segments of the bitstream to the different objects, which can be independently accessed and reconstructed at any up-to-lossless quality. Two fully 3D coding strategies are considered: Embedded Zerotree Coding (EZW-3D) and Multidimensional Layered Zero Coding (MLZC), both generalized for Region of Interest (ROI) based processing. In order to avoid artifacts along region boundaries, some extra coefficients must be encoded for each object. This gives rise to an overheading of the bitstream with respect to the case where the volume is encoded as a whole. The amount of such extra information depends on both the filter length and the decomposition depth. The system is characterized on a set of head magnetic resonance images. Results show that MLZC and EZW-3D have competitive performances. In particular, the best MLZC mode outperforms the other state-of-the-art techniques on one of the datasets for which results are available in the literature

On the design of fast and efficient wavelet image coders with reduced memory usage

Image compression is of great importance in multimedia systems and applications because it drastically reduces bandwidth requirements for transmission and memory requirements for storage. Although earlier standards for image compression were based on the Discrete Cosine Transform (DCT), a recently developed mathematical technique, called Discrete Wavelet Transform (DWT), has been found to be more efficient for image coding. Despite improvements in compression efficiency, wavelet image coders significantly increase memory usage and complexity when compared with DCT-based coders. A major reason for the high memory requirements is that the usual algorithm to compute the wavelet transform requires the entire image to be in memory. Although some proposals reduce the memory usage, they present problems that hinder their implementation. In addition, some wavelet image coders, like SPIHT (which has become a benchmark for wavelet coding), always need to hold the entire image in memory. Regarding the complexity of the coders, SPIHT can be considered quite complex because it performs bit-plane coding with multiple image scans. The wavelet-based JPEG 2000 standard is still more complex because it improves coding efficiency through time-consuming methods, such as an iterative optimization algorithm based on the Lagrange multiplier method, and high-order context modeling. In this thesis, we aim to reduce memory usage and complexity in wavelet-based image coding, while preserving compression efficiency. To this end, a run-length encoder and a tree-based wavelet encoder are proposed. In addition, a new algorithm to efficiently compute the wavelet transform is presented. This algorithm achieves low memory consumption using line-by-line processing, and it employs recursion to automatically place the order in which the wavelet transform is computed, solving some synchronization problems that have not been tackled by previous proposals. The proposed encodeOliver Gil, JS. (2006). On the design of fast and efficient wavelet image coders with reduced memory usage [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1826Palanci

3D encoding/2D decoding of medical data

We propose a fully three-dimensional wavelet-based coding system featuring 3D encoding/2D decoding functionalities. A fully three-dimensional transform is combined with context adaptive arithmetic coding; 2D decoding is enabled by encoding every 2D subband image independently. The system allows a finely graded up to lossless quality scalability on any 2D image of the dataset. Fast access to 2D images is obtained by decoding only the corresponding information thus avoiding the reconstruction of the entire volume. The performance has been evaluated on a set of volumetric data and compared to that provided by other 3D as well as 2D coding systems. Results show a substantial improvement in coding efficiency (up to 33%) on volumes featuring good correlation properties along the z axis. Even though we did not address the complexity issue, we expect a decoding time of the order of one second/image after optimization. In summary, the proposed 3D/2D Multidimensional Layered Zero Coding System (3D/2D MLZC) provides the improvement in compression efficiency attainable with 3D systems without sacrificing the effectiveness in accessing the single images characteristic of 2D ones

Lifting schemes for joint coding of stereoscopic pairs of satellite images

electronic version (5 pp.)International audienceStereo data compression is an important issue for the new generation of vision systems. In this paper, we are interested in lossless coding methods for stereo images allowing progressive reconstruction. Most of the existing approaches account for the mutual similarities between the left and the right images. More precisely, the disparity compensation process consists in predicting the right image from the left one based on the disparity map. Then, the disparity map, the reference image, and the residual image are encoded. In this work, we propose a novel approach based on the concept of vector lifting scheme. Its main feature is that it does not generate one residual image but two compact multiresolution representations of the left and the right views, driven by the underlying disparity map. Experimental results show a significant improvement using this technique compared with conventional methods