Compression of vector quantization code sequences based on code frequencies and spatial redundancies

Jari Kangas, Samuel Kaski

Abstract: Vector quantization (VQ) can be used to compress images with high compression ratios. The VQ methods produce a sequence of code values which identifies the codebook model vectors to be used as blocks of pixels in the decoded image. In this paper we define a novel non-lossy and computationally efficient method to further compress the code sequence based on the relative frequencies of the code values, and the spatial distribution of each code. In an example case we reduced the bit rate by 29%. A further reduction of 7 percentage units was obtained when the VQ codebook was produced by the self-organizing map (SOM) algorithm. A SOM codebook has the property that similar blocks have similar codes, which was used to take advantage of spatial redundancies in the image.