Inter-Image Similarity-Based Fast Adaptive Block Size Vector Quantizer for Image Coding

Abstract

Block coding is well known in the digital image coding literature. Vector quantization and transform coding are examples of well-known block coding techniques. Different images have many similar spatial blocks introducing inter-image similarity. The smaller the block size, the higher the inter-image similarity. In this paper, a new block coding algorithm based on inter-image similarity is proposed where it is claimed that any original image can be reconstructed from the blocks of any other image. The proposed algorithm is simply a vector quantization without the need to a codebook design algorithm and using matrix operations-based fast full search algorithm to find the local minimum root-mean-square error distortion measure to find the most similar code block to the input block. The proposed algorithm is applied in both spatial and transform domains with adaptive code block size. In the spatial domain, the encoding process has fidelity as high as 36.07[Formula: see text]dB with bit rate of 2.22[Formula: see text]bpp, while in the transform domain, the encoded image has good fidelity of 34.94[Formula: see text]dB with bit rate as low as 0.72[Formula: see text]bpp on the average. Moreover, the code image can be used as a secret key to provide secure communications.