Vector Quantization (VQ) is an important technology of image compression research in the recent years. Reducing encoding computation time and cutting down average encoding bit rates are the two important problems of its current research. In the present

many fast search encoding algorithm based on VQ are proposed. In order to get less encoding time and lower average encoding bit rates

a fast encoding algorithm

which integrates advance correlation predictive process with a fast search encoding algorithm

has been presented in this paper. After the current image block has been encoded

the encoding value of near neighbor image blocks is predicted

by virtue of their correlation to the current block. If predictive successes

the encoding value needs few bits to denote

otherwise it needs complex computation by Absolute Error Inequality Elimination algorithm (AEI)

and more bits to transfer. By this proposed way

the total encoding time decreases

and the total encoding bits saves. The simulation experiments show that the proposed algorithm consumes less time to encode

and needs lower average bit rates to transfer encoding results

against full search algorithm (FS)

while its encoding performance is close to that of full search algorithm.