Vector quantization provides better ratedistortion performance over scalar quantization even for a random vector with independent dimensions. However, the design and implementation complexity of vector quantizers is much higher than that of scalar quantizers. To reduce the complexity while achieving performance close to optimal vector quantization and better than scalar quantization, we propose...

We give a characterization of the quantization dimension of Borel probability measures on R in terms of ε-quantization numbers. Using this concept, we show that the upper rate distortion dimension is not greater than the upper quantization dimension of order one. We also prove that the upper quantization dimension of a product measure is not greater than the sum of that of its marginals. Finall...

We present a new procedure for quantizing field theory models on a noncommutative spacetime. The new quantization depends on the noncommutative parameter explicitly and reduces to the canonical quantization in the commutative limit. It is shown that a quantum field theory constructed by the new quantization yeilds exactly the same correlation functions as those of the commutative field theory, ...

Data quantization methods for continuous attributes play an extremely important role in artificial intelligence, data mining and machine learning because discrete values of attributes are required in most classification methods. In this paper, we present a supervised statistical data quantization method. It defines a quantization criterion based on the chi-square statistic to discover accurate ...

Quantization is the process of representing a large set of input values with a much smaller set. In signal processing and image processing, Vector Quantization is a classical quantization which extends the scalar quantization to multi-dimensional space. It is widely used in many applications such as data compression, data correction, pattern recognition, and density estimation. This project pro...

Network quantization is one of network compression techniques to reduce the redundancy of deep neural networks. It reduces the number of distinct network parameter values by quantization in order to save the storage for them. In this paper, we design network quantization schemes that minimize the performance loss due to quantization given a compression ratio constraint. We analyze the quantitat...