Quadrature mirror filter

About: Quadrature mirror filter is a research topic. Over the lifetime, 955 publications have been published within this topic receiving 28900 citations.

Optimized switched capacitor biquads for two-channel quadrature-mirror filter bank

Abstract: This paper deals with the design and optimization of biquadratic switched capacitor filters for 4th order quadrature-mirror filter banks allowing for real switch effects (e.g. on-state resistance, charge injection...) and the folded cascode OpAmp designed in 0.35 um CMOS technology. The filter is designed as a cascade of two biquads and each filter is separately optimized using the Differential Evolution Algorithm to obtain the required transfer function with deviation of less than ±0.1 dB from the designed transfer function. The filters process a baseband signals (0 - 8 kHz) using a clock frequency of 16 kHz. The resulting capacitor values are presented.

Design of linear-phase two-channel quadrature mirror filter banks using neural minor component analysis

Abstract: This paper presents a neural-based minor component analysis algorithm for designing quadrature mirror filter banks in the least-squares sense The objective function of the design problem is formulated as an eigenvector solving problem of a real, symmetric and positive-definite matrix An alternative minor component analysis algorithm based on neural learning rule is exploited to achieve the eigenfilter design of the quadrature mirror filter bank with accurate performance

Multiresolution Source Coding of High Quality Digital Audio Using Perfect Reconstruction M-Band Filter Banks

Abstract: In this paper we investigate multiresolution source coding of high quality digital audio using Perfect Reconstruction (PR) M-band filter banks. The time-frequency decomposition in the proposed coder consists of a three stage cascade of multi-channel filter banks. This representation enables a very close approximation to the critical band divisions with superior frequency selectivity compared to two-channel Quadrature Mirror Filter (QMF) Wavelet Packets. It also provides a variable time resolution which is advantageous for coding non-stationary or “transient-like” sources. The proposed coder provides very good quality down to and Uder 2 bitslsample for singlechannel Compact Disk (CD) audio.

Design and Performance Evaluation of a Quadrature Mirror Filter (QMF) Bank

Abstract: In this paper, a two channel Quadrature Mirror Filter (QMF) bank is designed and the performance is evaluated. A new method is developed to optimize the prototype filter response. The design problem is formulated as nonlinear unconstrained optimization of an objective function, which is weighted sum of square of error in passband, stop band and overall filter bank response. The proposed method gives better performance in terms of peak reconstruction error and computational time.

Image texture classification using Quadrature Mirror Filter bank in combination with Co—occurrence matrices

Abstract: This paper presents a method for the classification of textures using Quadrature Mirror Filter (QMF) bank subband decomposition in combination with statistical descriptors. In our combined method the QMF bank splits the input image into four subbands, and statisti-cal descriptors based on co-occurrence matrices are computed from the subsanzpled low-low band. The experiments demonstrate that the combined method have better classification performance than that of statistical descriptors computed from the co-occurrence matrices of the whole texture image. In addition, the experiments demonstrate that the combined method based on computationally efficient IIR QMF banks yields approximately the sameclassification results as the combined method based on classical FIR QMF banks. 1 Introduction Texture analysis plays an important role in such areas as medical diagnosis, remote sensing and industrial inspection. One important usage of texture analysis is to distinguish among various classes of textures. That is, given a sample texture, identify to which of a finite number of textureclasses the sample belongs.For the texture discrimination problem several texture descriptors have been proposed: Fourierpower spectrum [1], autocorrelation function [2], autoregressive moving average models [3], Markovrandom field models [4], digital filter banks [5] and spatial gray-level co-occurrence matrices [6].Among the different texture descriptors spatial gray-level co-occurrence matrices are cited in theliterature most frequently. A co-occurrence matrix is a second-order statistical measure of imagevariation. It contains information on the spatial relationship between pairs of gray levels of pixels.Co-occurrence matrices are seldomly used directly. Instead, features based on them are com-puted. Haralick eL al. [6] have suggested 14 textural features, which include both statistical andinformation theoretic measures.