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

Adapted generalized lifting schemes for scalable lossless image coding

International audienceStill image coding occasionally uses linear predictive coding together with multi-resolution decompositions, as may be found in several papers. Those related approaches do not take into account all the information available at the decoder in the prediction stage. In this paper, we introduce an adapted generalized lifting scheme in which the predictor is built upon two filters, leading to taking advantage of all this available information. With this structure included in a multi-resolution decomposition framework, we study two kinds of adaptation based on least squares estimation, according to different assumptions, which are either a global or a local second order stationarity of the image. The efficiency in lossless coding of these decompositions is shown on synthetic images and their performances are compared with those of well-known codecs (S+P, JPEG-LS, JPEG2000, CALIC) on actual images. Four images' families are distinguished: natural, MRI medical, satellite and textures associated with fingerprints. On natural and medical images, the performances of our codecs do not exceed those of classical codecs. Now for satellite images and textures, they present a slightly noticeable (about 0.05 to 0.08 bpp) coding gain compared to the others that permit a progressive coding in resolution, but with a greater coding time

A hybrid predictive technique for lossless image compression

Compression of images is of great interest in applications where efficiency with respect to data storage or transmission bandwidth is sought.The rapid growth of social media and digital networks have given rise to huge amount of image data being accessed and exchanged daily. However, the larger the image size, the longer it takes to transmit and archive. In other words, high quality images require huge amount of transmission bandwidth and storage space. Suitable image compression can help in reducing the image size and improving transmission speed. Lossless image compression is especially crucial in fields such as remote sensing healthcare network, security and military applications as the quality of images needs to be maintained to avoid any errors during analysis or diagnosis. In this paper, a hybrid prediction lossless image compression algorithm is proposed to address these issues. The algorithm is achieved by combining predictive Differential Pulse Code Modulation (DPCM) and Integer Wavelet Transform (IWT). Entropy and compression ratio calculation are used to analyze the performance of the designed coding. The analysis shows that the best hybrid predictive algorithm is the sequence of DPCM-IWT-Huffman which has bits sizes reduced by 36%, 48%, 34% and 13% for tested images of Lena, Cameraman, Pepper and Baboon, respectively

A new frequency analysis for diagnosis of bearing defects in induction motors using the adaptive lifting scheme of wavelet transforms

This work describes a novel and effective application of the adaptive wavelet transform for the detection of bearing faults on induction motor stator current. This transform is based on a three-step nonlinear lifting scheme: a fixed prediction followed by a space-varying update and a no additive prediction. This transformation technique is used in a diversity of applications in digital signal processing and the transmission or storage of sampled data (notably the compression of the sound, or physical measurements of accuracy). Many faults in induction motor have been identified as bearing defects, rotor defects and external defects. Experimental results confirm the utility and the effectiveness of the proposed method for outer raceway fault diagnosis under no load and full load conditions

Hierarchical Lossless Image Compression for Telemedicine Applications

AbstractThe main aim of hierarchical lossless image compression is to improve accuracy, reduce the bit rate and improve the compression efficiency for the storage and transmission of the medical images while maintain an acceptable image quality for diagnosis purpose. The cost and limitation in bandwidth of wireless channels has made compression is necessity in today's era. In medical images, the contextual region is an area which contains an important information and must be transmitted without distortion. In this paper the selected region of the image is encoded with Adaptive Multiwavelet Transform AMWT) using Multi Dimensional Layered Zero Coding (MLZC). Experimental results shows that Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), Mean Structural Similarity Index (MSSIM) performance is high and Root Mean Square Error (RMSE), Mean Absolute Error (MAE) values are low, and moderate Compression Ratio (CR) at high Bits Per Pixel (BPP) when compared to the integer wavelet and multiwavelet transform

Vector Lifting Schemes for Stereo Image Coding

International audienceMany research efforts have been devoted to the improvement of stereo image coding techniques for storage or transmission. In this paper, we are mainly interested in lossyto- lossless coding schemes for stereo images allowing progressive reconstruction. The most commonly used approaches for stereo compression are based on disparity compensation techniques. The basic principle involved in this technique first consists of estimating the disparity map. Then, one image is considered as a reference and the other is predicted in order to generate a residual image. In this work, we propose a novel approach, based on Vector Lifting Schemes (VLS), which offers the advantage of generating two compact multiresolution representations of the left and the right views. We present two versions of this new scheme. A theoretical analysis of the performance of the considered VLS is also conducted. Experimental results indicate a significant improvement using the proposed structures compared with conventional methods