The following software implementations have been produced under this project:

**The implementation of the Bjontegaard metric**

Source code: Python Source codes and a sample data file to calculate Bjontegaard metric

User manual: Brief description of the source code functions and usage examples.

**Frame Classification strategy for Diagnostically lossless compression of coronary angiograms**

Source code: Matlab codes and demo file to achieve frame classification of coronary angiograms

User manual: Brief description of the source code functions and usage examples.

Frame visual evaluation: An example irrelevant frame, and its decompressed frames after complete lossy compression and 1/10 lossy compression

**Background Suppression strategy for Diagnostically lossless compression of X-ray angiography images**

Source code: Matlab codes and demo file to achieve background suppression of X-ray angiography images

User manual: Brief description of the source code functions and usage examples

**Sided PPA (Principal Polynomial Analysis)**

Source code: Matlab code

**Microarray Distortion Metric (MDM) implementation**

Source code: as described in Miguel Hernández-Cabronero, Victor Sanchez, Michael W. Marcellin, Joan Serra-Sagristà, “A distortion metric for the lossy compression of DNA microarray images”, In proceedings of the IEEE International Data Compression Conference, DCC 2013**Simple Regression toolbox (simpleR)**

Matlab code: the simple Regression toolbox, simpleR, contains a set of functions in Matlab to illustrate the capabilities of several statistical regression algorithms. simpleR contains simple educational code for linear regression (LR), decision trees (TREE), neural networks (NN), support vector regression (SVR), kernel ridge regression (KRR), aka Least Squares SVM, Gaussian Process Regression (GPR), and Variational Heteroscedastic Gaussian Process Regression (VHGPR). This is just a demo providing a default initialization. Training is not at all optimized. Other initializations, optimization techniques, and training strategies may be of course better suited to achieve improved results in this or other problems. We just did it in the standard way for illustration and educational purposes, as well as to disseminate these models.**PPA: Principal Polynomial Analysis**

Matlab code: A new framework for manifold learning based on the use of a sequence of principal polynomials that capture the eventually nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) is shown to generalize PCA by admitting curves instead of straight lines.**HOCCA: higher order canonical correlation analysis**

Matlab code: the HOCCA Toolbox is a Matlab Toolbox for Higher Order Canonical Corrrelation Analysis. HOCCA analyzes several data sets jointly: It finds in each data set independent components which are related across the data sets. HOCCA was used to derive spatio-chromatic receptive fields from calibrated images.**SSMA**

Matlab code: the SSMA Toolbox is a Matlab Toolbox for the semisupervised manifold alignment for the submitted paper:

Semisupervised Manifold Alignment of Multimodal Remote Sensing Images.

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