Our method is based on vector quantization, which is one of the popular image compression techniques.
This paper proposes a nearest neighbor classifier design method based on vector quantization (VQ).
In this paper, we propose a new feedback design based on vector quantization.
We propose algorithms based on uniform quantization and sequential search to solve the corresponding non-convex optimization problems.
New evidence based on the quantization effect indicates a high degree of adjacent re-entry in regime I for the lower molecular weight fractions.
We design a distributed encoding-decoding scheme based on difference quantization with dynamic scaling and a control protocol based on a symmetric compensation method.
Therefore, the SNR increases with the quantization index and scheduling based on the quantization indices works the same as scheduling based on the SNRs.
However, most previous research works, such as [3 5], were based on suboptimal quantization method.
We also consider SP-DKLT code designs based on scalar quantization.
Since ICLD can be converted to azimuth, the quantization of ICLD can be easily achieved based on the quantization codebook of azimuth as the process procedure illustrated in Fig. 8. Fig. 8 Framework of ICLD quantization.
