Color Image Compression Based on Quaternion Neural Network Principal Component Analysis

A color image compression algorithm based on quaternion neural network approach is proposed. The original RGB based color image of Lena can be firstly modeled as pure imaginary quaternion matrix, i.e. any pixel of R,G,B corresponding to the I,J,K imaginary axis , to ensure the integrity of pixel in the computation. The obtained quaternion matrix can be split up into 8 × 8 sub-blocks and vector quantization to make up of a new sample set. This sample set then is used to train the quaternion neural network adopting quaternion Generalized Hebbian Algorithm (QGHA), acquiring a quaternion weight coefficient that can get the principal components (PCs), the weight can be used to compress and reconstruct the image. Experimental results show the proposed algorithm is effective, the weight trained from image of Lena is successfully used to other images´ compression and reconstruction.