In this paper we propose a noise reduction system for single channel speech signals. The system comprises of a 32-point FFT based spectral subtraction method, a variable least mean squares (LMS) filter and a 3-state voice activity detector. The LMS filter is designed to remove musical noise generated during spectral subtraction. It takes the original signal and the result of spectral subtraction as its inputs, and exploits the statistical differences between musical noise and speech to give a natural sounding output. The voice activity detector updates the noise spectrum estimate for spectral subtraction and controls the LMS filter parameters to make it adapt better to the incoming signal. Test cases involving three different non-stationary noise environments resulted in an average improvement of 6dB in the SNR after processing with low musical noise.

Summary form only given. Wavelets intrinsically give us the capability of localized signal decomposition and analysis in space and frequency. We present the recursive merge filter (RMF) discrete wavelet transform (DWT) algorithm, that organizes intermediate coefficients with respect to both space and frequency. This allows close coupling and pipelining with the encoder, fine grained coding, and "cheap growing" of larger DWT from smaller ones in a constant number of filter operations. The RMF algorithm computes the DWT of an array of length N in a bottom-up fashion, by successively "merging" two smaller DWT (four in 2D), and applying the wavelet filter only on the "smooth" or DC coefficients.

Summary form only given. The contribution of this paper is an extension of the pruned tree-structured hierarchical vector quantization (PTSHVQ) algorithm, with a focus on designing and benchmarking a complete Internet-aware system for the real-time delivery of compressed images. We extend the PTSHVQ algorithm to enable the encoder to use a combination of "coarse" and "fine" rate control mechanisms. The coarse rate control is achieved by switching between successive HVQ lookup tables, thereby changing the bit rate on a logarithmic scale, e.g., to give 4, 2, 1, 0.5 bpp, etc. The fine rate control is achieved by sub-setting into the address space of the current HVQ lookup table, e.g., generating 4, 3.5, 3, 2.5 bpp. The combination of these two strategies of switching between HVQ stages and subsetting the available range of addresses at each stage gives us a much larger dynamic range as well as finer calibration of operational bit rates than any existing HVQ system. Organizing the codebooks into binary tree structures by using the greedy BFOS algorithm guarantees locally optimal rate-distortion behavior at each operational bit rate. The function of the distributed transcoder is to enable the regulation of operational bandwidth over a heterogeneous network, thereby guaranteeing fairness in broadcast type applications.

No abstract available.

Summary form only given. We propose a video coding and delivery scheme which is geared towards low bit-rate and real-time performance requirements. We use a hybrid vector quantization scheme, with finite state wavelet-based hierarchical lookup vector quantization (FSWHVQ) for coding motion vectors, which embeds the Horn and Schunck optical flow algorithm in table-lookups, and uses lattice vector quantization (LVQ) for the prediction errors. This video coding scheme is both fast (table-lookups) and accurate (dense motion field), and avoids the blocking artifacts and poor prediction which plagues block coding schemes at low bit rates. For restricted image compression/transmission scenarios like teleconferencing, for which a good training set may be available, the FSWHVQ scheme may be viewed as storing as an internal representation in its lookup tables, a valid and complete model of the problem domain. Two row-wise lookups quantize the row-wise pixel values to give two table indices, Y0 and Y1, followed by a column-wise second-level table lookup, which quantizes the column-wise indices into one index, ZO. Horn and Schunck's optical flow method applies spatial and temporal derivative masks, e.g., of size 2/spl times/2, at corresponding spatial locations of the current and previous images. These mask applications are embedded inside table-lookups. The motion field averages (u/sub av/,v/sub av/), and the previous frame, I/sub t-I/, together comprise the "state", and are updated at each frame. The prediction errors are encoded with LVQ, as the error vectors are small (due to the accurate optical flow-based motion estimation), but more randomly distributed. It is possible to handle large displacements, by using the optical flow computations on the "smooth" bands in the DWT (discrete wavelet transform) domain.

We propose a real-time image coding system, which is capable of adapting instantaneously to the available channel bandwidth. The range of operational bandwidths for this system has a finer calibration than ordinary hierarchical vector quantization (HVQ) or wavelet based hierarchical vector quantization (WHVQ) methods suggested in the literature. These properties make it very attractive for networks with fluctuating available bandwidth, like the Internet. All encoder and decoder operations are strictly constant time per pixel, proceeding through table lookups, and are intrinsically suitable for parallel and hardware implementation.

We propose a video coding and delivery scheme which is geared towards low bit-rate and real-time performance requirements. We use a finite state wavelet-based hierarchical lookup vector quantization (FSWHVQ) scheme, which embeds the optical flow calculations in table-lookups. This video coding scheme is both fast (table-lookups) and accurate (dense motion field), and avoids the blocking artifacts and poor prediction which plagues block coding schemes at low bit rates. For restricted image compression/transmission scenarios like teleconferencing, for which a good training set may be available, the FSWHVQ scheme may be viewed as storing an internal representation in its lookup tables, a valid and complete model of the problem domain.