Improving spatial vector quantization for image compression by use of a quadtree scheme. Application to echoendoscopic image compression

Image coding using vector quantization is an interesting approach for image compression. Among the different existing algorithms, Kohonen's self-organizing feature maps (SOFMs) are well suited for designing the codebooks because of their specific properties. Moreover, the use of this method for compression gives, in the same process, basic information on the image content, but to preserve the diagnostic accuracy in echoendoscopic images, it is necessary to take small codewords, generally not greater than subimages of 3 by 3 pixels, which limits the compression rate. We propose to improve the compression rate by using four codebooks, with codewords of different size. Image analysis for coding uses a quadtree scheme. Results are compared with those obtained using the standard JPEG image compression algorithm