Vector Quantization is lossy data compression technique and has various applications. Key to Vector Quantization is good codebook. Once the codebook size is fixed then for any codebook generation algorithm the MSE reaches a value beyond which it cannot be reduced unless the codebook size is increased. In this paper we are proposing two-level codebook generation algorithm which reduces mean squa...

The research described in this paper uses the CMAES evolution strategy to optimize matched forward and inverse transform pairs for the compression and reconstruction of images transmitted from Mars rovers under conditions subject to quantization error. Our best transforms outperform both the integer and floating-point implementations of the 2/6 wavelet, substantially reducing error in reconstru...

We have investigated metastability errors in algorithmic A/D converters. When the input signal is inside a window, VM, a metastability errors appear as a spike in the output code. These errors looks like white noise in the frequency domain, and can therefore be compared with the quantization noise. For an n-bit ADC, the probability of a metastability error in bit j is pMj = VM · 2 (n-j), error ...

Speech coding systems for mobile communication have to cope with noisy channels. In particular, vector quantization as central data reduction scheme is highly sensitive to transmission errors due to the low redundancy in the encoded data. Here we present three methods for the design of a vector quantizer with enhanced robustness against transmission errors. First the optimization of the index a...

This paper deals with the problem of reconstructing nonuniformly sampled bandlimited signals. This problem arises in, e.g., time-interleaved analog-to-digital converter (ADC) systems due to time-skew errors. A synthesis system composed of (ideal) digital fractional delay filters for the reconstruction was recently proposed in [1]. In this paper, we briefly recapitulate the theory and continue w...

In this article, we study the approximation of a probability measure μ on R by its empirical measure μ̂N interpreted as a random quantization. As error criterion we consider an averaged p-th moment Wasserstein metric. In the case where 2p < d, we establish fine upper and lower bounds for the error, a high-resolution formula. Moreover, we provide a universal estimate based on moments, a Pierce ty...

In this paper, we reinvestigate the solution for chaotic time series prediction problem using neural network approach. The nature of this problem is such that the data sequences are never repeated, but they are rather in chaotic region. However, these data sequences are correlated between past, present, and future data in high order. We use Cascade Error Projection (CEP) learning algorithm to c...

A new vector quantization method is proposed which generates codebooks incrementally. New vectors are inserted in areas of the input vector space where the quantization error is especially high until the desired number of codebook vectors is reached. A one-dimensional topological neighborhood makes it possible to interpolate new vectors from existing ones. Vectors not contributing to error mini...

Previous research demonstrated that a genetic algorithm (GA) can utilize supercomputers to evolve image compression and reconstruction transforms that reduce mean squared error (MSE) by more than 22% (1.126 dB) under conditions subject to quantization, while continuing to average the same amount of compression as the Daubechies-4 (D4) wavelet. This paper describes subsequent research that exten...

Spatial filtering using adaptive or smart antennas has emerged as a promising technique to improve the performance of cellular mobile systems. Due to the digital nature of the control of spatial filtering a quantization error is readily present. This is due to the fact that weights and phase information is provided with the aid of finite wordlength processors. This paper presents the developmen...