Fundamentals of digital image processing
Fundamentals of digital image processing
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Stability analysis of Oja-RLS learning rule
Fundamenta Informaticae - Special issue on image compression
Principal component extraction using recursive least squares learning
IEEE Transactions on Neural Networks
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Image compression is performed by 8 × 8 block transform based on approximated 2D Karhunen Loeve Transform. The transform matrix W is produced by eight pass, modified Oja-RLS neural algorithm which uses the learning vectors creating the image domain subdivision into 8 × 1 blocks. In transform domain, the stages of quantisation and entropy coding follow exactly JPEG standard principles. It appears that for images of natural scenes, the new scheme outperforms significantly JPEG standard: at the same bitrates it gives up to two decibels increase of PSNR measure while at the same image quality it gives up to 50% lower bitrates. Despite the time complexity of the proposed scheme is higher than JPEG time complexity, it is practical method for handling still images, as C++ implementation on PC platform, can encode and decode for instance LENA image in less than two seconds.