Improving quality of medical image compression using biorthogonal CDF wavelet based on lifting scheme and SPIHT coding

As the coming era is that of digitized medical information, an important challenge to deal with is the storage and transmission requirements of enormous data, including medical images. Compression is one of the indispensable techniques to solve this problem. In this work, we propose an algorithm for medical image compression based on a biorthogonal wavelet transform CDF 9/7 coupled with SPIHT coding algorithm, of which we applied the lifting structure to improve the drawbacks of wavelet transform. In order to enhance the compression by our algorithm, we have compared the results obtained with wavelet based filters bank. Experimental results show that the proposed algorithm is superior to traditional methods in both lossy and lossless compression for all tested images. Our algorithm provides very important PSNR and MSSIM values for MRI images

Scalable video compression with optimized visual performance and random accessibility

This thesis is concerned with maximizing the coding efficiency, random accessibility and visual performance of scalable compressed video. The unifying theme behind this work is the use of finely embedded localized coding structures, which govern the extent to which these goals may be jointly achieved. The first part focuses on scalable volumetric image compression. We investigate 3D transform and coding techniques which exploit inter-slice statistical redundancies without compromising slice accessibility. Our study shows that the motion-compensated temporal discrete wavelet transform (MC-TDWT) practically achieves an upper bound to the compression efficiency of slice transforms. From a video coding perspective, we find that most of the coding gain is attributed to offsetting the learning penalty in adaptive arithmetic coding through 3D code-block extension, rather than inter-frame context modelling. The second aspect of this thesis examines random accessibility. Accessibility refers to the ease with which a region of interest is accessed (subband samples needed for reconstruction are retrieved) from a compressed video bitstream, subject to spatiotemporal code-block constraints. We investigate the fundamental implications of motion compensation for random access efficiency and the compression performance of scalable interactive video. We demonstrate that inclusion of motion compensation operators within the lifting steps of a temporal subband transform incurs a random access penalty which depends on the characteristics of the motion field. The final aspect of this thesis aims to minimize the perceptual impact of visible distortion in scalable reconstructed video. We present a visual optimization strategy based on distortion scaling which raises the distortion-length slope of perceptually significant samples. This alters the codestream embedding order during post-compression rate-distortion optimization, thus allowing visually sensitive sites to be encoded with higher fidelity at a given bit-rate. For visual sensitivity analysis, we propose a contrast perception model that incorporates an adaptive masking slope. This versatile feature provides a context which models perceptual significance. It enables scene structures that otherwise suffer significant degradation to be preserved at lower bit-rates. The novelty in our approach derives from a set of "perceptual mappings" which account for quantization noise shaping effects induced by motion-compensated temporal synthesis. The proposed technique reduces wavelet compression artefacts and improves the perceptual quality of video

Image Processing Using FPGAs

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs

Measurement and analysis of natural video masked dynamic discrete cosine transform noise detectability

Lossy video compression lowers fidelity and can leave visual artifacts. Current video compression algorithms are guided by quality assessment tools designed around subjective data based on aggressive video compression. However, most consumer video is of high quality with few detectable visual artifacts. A better understanding of the visual detectability of such artifacts is crucial for improved video compression. Current techniques of predicting artifact detectability in videos have been largely guided by studies using no masks or using still-image masks. There is limited data quantifying the detectability of compression artifacts masked by natural videos. In this paper, we investigate the effect of natural video masks on the detectability of time-varying DCT basis function compression artifacts. We validate the findings from Watson et al. [JEI 2001], who found that as these artifacts increase in spatial and temporal frequency, detection contrast thresholds tend to increase. We extend this work by presenting compression artifacts with natural videos; when artifacts are shown with natural videos, this relationship between artifact spatial frequency and threshold is reduced or even reversed (our data suggests that some natural videos make targets easier to detect). More generally, our results demonstrate that different videos have different effects on artifact detectability. A model using target and video properties to predict target detection thresholds summarizes these results. We expand these results to examine the relationship between mask luminance, contrast, and playback rates on compression artifact detectability. We also examine how the detectability of targets that are spatially correlated with mask content differ from the detectability of uncorrelated targets. This paper's data serves to fill-in an understanding gap in natural-video masking, and it supports future video compression research