Controlled experiments with novice treemap users and real data highlight the strengths of
treemaps and provide direction for improvement. Issues discussed include experimental …

In clustering the training sequence (TS), K-means algorithm tries to find empirically optimal
representative vectors that achieve the empirical minimum to inductively design optimal …

Channel-optimized vector quantization (COVQ) has proven to be an effective joint source-
channel coding technique that makes the underlying quantizer robust to channel noise …

The vector quantizer (VQ) codebook is usually designed by clustering a training sequence
(TS) drawn from the underlying distribution function. In order to cluster a TS, we may use the …

In this paper, in order to reduce blocking artifacts in vector-quantized images, we propose a
new postprocessing algorithm, based on the projection onto a subset of quantization …

We introduce multivariate block polar quantization (MBPQ). MBPQ minimizes the weighted
squared-error distortion for a set of complex variables representing one block of a signal …

When we design a robust vector quantizer (VQ) for noisy channels, an appropriate index
assignment function should be contrived to minimize the channel-error effect. For relatively …

디지털 신호의 양을 줄이기 위한 손실 소스 부호화에서 양자화는 필수적이다. 이때 보다
효율적인 양자화를 위해서는 벡터 양자기 (vector quantizer: VQ) 를 사용하는데, 벡터의 차수 …

K-means is a widely used method for the process of vector quantization in image retrieval,
and its results will directly affect the subsequent retrieval quality. Although k-means is …

Channel optimized vector quantization (COVQ), as a joint source-channel coding scheme,
has proven to perform well in compressing a source and making the resulting quantizer …