Vector quantization and signal compression
Vector quantization and signal compression
Performance improvement of vector quantization by using threshold
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part III
An efficient computation of Euclidean distances using approximated look-up table
IEEE Transactions on Circuits and Systems for Video Technology
An efficient Euclidean distance computation for vector quantization using a truncated look-up table
IEEE Transactions on Circuits and Systems for Video Technology
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Vector quantization (VQ) is an elementary technique for image compression. However, searching for the nearest codeword in a codebook is time-consuming. The existing schemes focus on software-based implementation to reduce the computation. However, such schemes also incur extra computation and limit the improvement. In this paper, we propose a hardware-based scheme “Pruned Look-Up Table” (PLUT) which could prune possible codewords. The scheme is based on the observation that the minimum one-dimensional distance between the tested vector and its matched codeword is usually small. The observation inspires us to select likely codewords by the one-dimensional distance, which is represented by bitmaps. With the bitmaps containing the positional information to represent the geometric relation within codewords, the hardware implementation can succinctly reduce the required computation of VQ. Simulation results demonstrate that the proposed scheme can eliminate more than 75% computation with an extra storage of 128 Kbytes.