This chapter describes digital image compression techniques, and the JPEG image compression standard. Digital image compression techniques are based on algorithms such as the Wavelet transform, Fractal images, Vector Quantisation and Discrete Cosine Transform (DCT). The digital image compression technique developed by the Joint Photographic Experts Group (JPEG) is mainly based on the quantisation of the DCT.

Digital Image Compression Techniques

https://www.cscjournals.org/download/issuearchive/IJIP/volume6/IJIP_V6_I1.pdf

International journal of image processing (ijip)

Medical image compression plays a key role as hospitals move towards filmless imaging and go completely digital. Image compression will allow Picture Archiving and Communication Systems (PACS) to reduce the file sizes on their storage requirements while maintaining relevant diagnostic information. Lossy compression schemes are not used in medical image compression due to possible loss of useful clinical information and as operations like enhancement may lead to further degradations in the lossy compression. Medical imaging poses the great challenge of having compression algorithms that reduce the loss of fidelity as much as possible so as not to contribute to diagnostic errors and yet have high compression rates for reduced storage and transmission time. This paper outlines the comparison of compression methods such as Shape- Adaptive Wavelet Transform and Scaling Based ROI, JPEG2000 Max-Shift ROI Coding, JPEG2000 Scaling- Based ROI Coding, Discrete Cosine Transform, Discrete Wavelet Transform and Subband Block Hierarchical Partitioning on the basis of compression ratio and compression quality.

/pdf/a-survey-on-various-compression-methods-for-medical-images-s2tstvmzov.pdf

A Survey on Various Compression Methods for Medical Images

Medical images require compression, before transmission or storage, due to constrained bandwidth and storage capacity. An ideal image compression system must yield high-quality compressed image with high compression ratio. In this paper, Haar wavelet transform and discrete cosine transform are considered and a neural network is trained to relate the X-ray image contents to their ideal compression method and their optimum compression ratio.

/pdf/backpropagation-neural-network-implementation-for-medical-17gdv0scvq.pdf

Backpropagation Neural Network Implementation for Medical Image Compression

Implementing lossless compression during image processing by integrated approach

As medical/biological imaging facilities move towards complete film-less imaging, compression plays a key role. Although lossy compression techniques yield high compression rates, the medical community has been reluctant to adopt these methods, largely for legal reasons, and has instead relied on lossless compression techniques that yield low compression rates. The true goal is to maximise compression while maintaining clinical relevance and balancing legal risk. This paper proposes a novel model-based compression technique that makes use of clinically relevant regions as defined by radiologists. Lossless compression is used in these clinically relevant regions, and lossy compression is used everywhere else.

https://www.vast.uccs.edu/~tboult/PAPERS/IJBRA-06-Zukowski-Boult-Medical-Image-Compression.pdf

Matthew J. Zukoski

Papers

A novel approach to medical image compression