Low complexity index-compressed vector quantization for image compression

This paper proposes a novel lossless index compression algorithm that explores the interblock correlation in the index domain and the property of the codebook ordering. The goal of this algorithm is to improve the performance of the VQ scheme at a low bit rate while keeping low computation complexity. In this algorithm, the closest codeword in the codebook is searched for each input vector. Then, the resultant index is compared with the previously encoded indices in a predefined search order to see whether the same index value can be found in the neighboring region. Besides, the relative addressing technique is employed to encode the current index if the same index value can not be found in the region. According to the results, the newly proposed algorithm achieves significant reduction of bit rate without introducing extra coding distortion. It is concluded that our algorithm is very efficient and effective for image vector quantization.