Evolutionary clustering based vector quantization and SPIHT coding for image compression

Background Digital pathology provides a digital environment for the management and interpretation of pathological images and associated data. It is becoming increasing popular to use modern computer based tools and applications in pathological education, tissue based research and clinical diagnosis. Uptake of this new technology is stymied by its single user orientation and its prerequisite and cumbersome combination of mouse and keyboard for navigation and annotation. Methodology In this study we developed SurfaceSlide, a dedicated viewing platform which enables the navigation and annotation of gigapixel digitised pathological images using fingertip touch. SurfaceSlide was developed using the Microsoft Surface, a 30 inch multitouch tabletop computing platform. SurfaceSlide users can perform direct panning and zooming operations on digitised slide images. These images are downloaded onto the Microsoft Surface platform from a remote server on-demand. Users can also draw annotations and key in texts using an on-screen virtual keyboard. We also developed a smart caching protocol which caches the surrounding regions of a field of view in multi-resolutions thus providing a smooth and vivid user experience and reducing the delay for image downloading from the internet. We compared the usability of SurfaceSlide against Aperio ImageScope and PathXL online viewer. Conclusion SurfaceSlide is intuitive, fast and easy to use. SurfaceSlide represents the most direct, effective and intimate human-digital slide interaction experience. It is expected that SurfaceSlide will significantly enhance digital pathology tools and applications in education and clinical practice.

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SurfaceSlide: A Multitouch Digital Pathology Platform

http://deic.uab.cat/~jbartrina/docs/articles/2009_09-BAS-ELSEVIER_CVIU-JPEG2000_ROI_Coding_Through_Component_Priority_for_Digital_Mammography.pdf

JPEG2000 ROI coding through component priority for digital mammography

To address the problem of ever-growing scientific data sizes making data movement a major hindrance to analysis, we introduce a novel encoding for scalar fields: a unified tree of resolution and precision, specifically constructed so that valid cuts correspond to sensible approximations of the original field in the precision-resolution space. Furthermore, we introduce a highly flexible encoding of such trees that forms a parameterized family of data hierarchies. We discuss how different parameter choices lead to different trade-offs in practice, and show how specific choices result in known data representation schemes such as zfp [52] , idx [58] , and jpeg 2000 [76] . Finally, we provide system-level details and empirical evidence on how such hierarchies facilitate common approximate queries with minimal data movement and time, using real-world data sets ranging from a few gigabytes to nearly a terabyte in size. Experiments suggest that our new strategy of combining reductions in resolution and precision is competitive with state-of-the-art compression techniques with respect to data quality, while being significantly more flexible and orders of magnitude faster, and requiring significantly reduced resources.

Efficient and Flexible Hierarchical Data Layouts for a Unified Encoding of Scalar Field Precision and Resolution

The proposed work is hybridizing self organizing map (SOM) and wavelet transform for performing image compression. The novelty in this work is applying discrete wavelet transform (DWT) on the code vector obtained from SOM after vector quantization and storing only the approximation coefficients along with the index values of the SOM. The experimental results of the proposed method show better compression and better peak signal to noise ratio (PSNR) value of the decompressed image.

Self organizing map and wavelet based image compression

An image coding algorithm, progressive resolution coding (PROGRES), for a high-speed resolution scalable decoding is proposed. The algorithm is designed based on a prediction of the decaying dynamic ranges of wavelet subbands. Most interestingly, because of the syntactic relationship between two coders, the proposed method costs an amount of bits very similar to that used by uncoded (i.e., not entropy coded) SPIHT. The algorithm bypasses bit-plane coding and complicated list processing of SPIHT in order to obtain a considerable speed improvement, giving up quality scalability, but without compromising coding efficiency. Since each tree of coefficients is separately coded, where the root of the tree corresponds to the coefficient in LL subband, the algorithm is easily extensible to random access decoding. The algorithm is designed and implemented for both 2D and 3D wavelet subbands. Experiments show that the decoding speeds of the proposed coding model are four times and nine times faster than uncoded 2D-SPIHT and 3D-SPIHT, respectively, with almost the same decoded quality. The higher decoding speed gain in a larger image source validates the suitability of the proposed method to very large scale image encoding and decoding. In the Appendix, we explain the syntactic relationship of the proposed PROGRES method to uncoded SPIHT, and demonstrate that, in the lossless case, the bits sent to the codestream for each algorithm are identical, except that they are sent in different order.

W.A. Pearlman

Papers

Hierarchical Dynamic Range Coding of Wavelet Subbands for Fast and Efficient Image Decompression