AT&T Bell Lab., Murray Hill, NJ.
IEEE Trans Image Process. 1992;1(2):170-85. doi: 10.1109/83.136594.
A class of vector quantizers with memory that are known as finite state vector quantizers (FSVQs) in the image coding framework is investigated. Two FSVQ designs, namely side match vector quantizers (SMVQs) and overlap match vector quantizers (OMVQs), are introduced. These designs take advantage of the 2-D spatial contiguity of pixel vectors as well as the high spatial correlation of pixels in typical gray-level images. SMVQ and OMVQ try to minimize the granular noise that causes visible pixel block boundaries in ordinary VQ. For 512 by 512 gray-level images, SMVQ and OMVQ can achieve communication quality reproduction at an average of 1/2 b/pixel per image frame, and acceptable quality reproduction. Because block boundaries are less visible, the perceived improvement in quality over ordinary VQ is even greater. Owing to the structure of SMVQ and OMVQ, simple variable length noiseless codes can achieve as much as 60% bit rate reduction over fixed-length noiseless codes.
研究了一类具有记忆的矢量量化器，在图像编码框架中称为有限状态矢量量化器（FSVQ）。引入了两种 FSVQ 设计，即边匹配矢量量化器（SMVQ）和重叠匹配矢量量化器（OMVQ）。这些设计利用了像素向量的二维空间连续性以及典型灰度图像中像素的高空间相关性。SMVQ 和 OMVQ 试图最小化在普通 VQ 中导致可见像素块边界的颗粒噪声。对于 512 乘 512 的灰度图像，SMVQ 和 OMVQ 可以在每个图像帧平均 1/2 位/像素的通信质量再现，并且可以接受质量再现。由于块边界不太明显，因此与普通 VQ 相比，感知质量的提高甚至更大。由于 SMVQ 和 OMVQ 的结构，简单的变长无噪码可以比固定长度无噪码减少多达 60%的比特率。
