Improved Codebook design for Vector Quantization on Orthogonal Polynomials based Transform Coding

Abstract: In this paper, an improved algorithm for codebook design is proposed for vector quantization (VQ) in image coding. Based on proposed Orthogonal Polynomials transformation, significant features of the training image vectors are extracted. By partitioning these feature vectors into a binary tree an improved codebook design algorithm is developed Each feature vector at a non-terminal node of the binary tree is directed to one of the two descendants by comparing a single feature associated with that node to a threshold. The binary tree codebook is used for encoding and decoding the feature vectors. In the decoding process the feature vectors are subjected to inverse transformation with the help of basis functions of the proposed Orthogonal Polynomials based transformation to get back the approximated input image training vectors. The results of the proposed algorithm are compared with the VQ using Discrete Cosine Transform (DCT) and Pairwise Nearest Neighbor (PNN) algorithm. uyThe new algorithm results in a considerable reduction in computation time and provides better reconstructed picture quality. Keywords: Image Coding, Orthogonal Polynomials, Binary tree classifier, Vector Quantization.