Vector Quantization has emerged as an efficient data compression tool for compressing speech and image data. We develop efficient systolic architecture implementations of Tree-Search Vector Quantizers (TSVQ) and Finite-State Vector Quantizers (FSVQ). Our TSVQ architecture consists of a linear array of processors, each processor performing the computations required at one level of the binary tree. Encoding is performed in a pipeline fashion with each processor generating a portion of the path through the tree. The final processor returns the complete index. Data and control flow from processor to processor along the pipeline and no global control signals are needed. The FSVQ architecture for image coding consists of a linear array of TSVQ processors with each processor operating on a separate column of the input image. The number of processors needed depends on the latency of the TSVQ and is independent of the size of the image. We also develop implementations of Scalar and Inverse Scalar Quantizers for use in transform coding applications.