Neural network-based codebook search for image compression

This paper presents an efficient and fast encoding of still images using the feedforward neural network technique for codebook search. The image to be coded is first clustered into a small subset of neighboring images and then the neural network-based encoder is used to find the best matching code sequences in the codebook. This subset is then used as a candidate set and an exhaustive search is then performed within this subset to find an optimal code sequence which minimizes the perceptual error between coded and decoded images. In this work, a generic codebook is developed using non-causal differential pulse coded modulation (DPCM) with residual mean removal and vector quantization using Linde, Buzo and Gray (1980) method. The codebook is analyzed to identify a pattern in the codebook. This pattern is used to train a neural network to obtain the approximate index of the pattern in the codebook. Then, an extensive search is done around this approximate position identified by the neural network to obtain the nearest neighbor of the pattern. Since the candidate set is usually much smaller that the whole code book, there is a substantial saving in codebook search time for coding an image as compared to the traditional method using full codebook search by the LBG algorithm.