Bhaskar Ramamurthi

Classified Vector Quantization of Images

Vector quantization (VQ) provides many attractive features for image coding with high compression ratios. However, initial studies of image coding with VQ have revealed several difficulties, most notably edge degradation and high computational complexity. We address these two problems and propose a new coding method, classified vector quantization (CVQ), which is based on a composite source model. Blocks with distinct perceptual features, such as edges, are generated from different subsources, i.e., belong to different classes. In CVQ, a classifier determines the class for each block, and the block is then coded with a vector quantizer designed specifically for that class. We obtain better perceptual quality with significantly lower complexity with CVQ when compared to ordinary VQ. We demonstrate with CVQ visual quality which is comparable to that produced by existing coders of similar complexity, for rates in the range 0.6-1.0 bits/pixel.

Nonlinear space-variant postprocessing of block coded images

An important application of spatial filtering techniques is in the postprocessing of images degraded by coding. Linear, space-invariant filters are inadequate to reduce the noise produced by block coders. The noise in block coded images is correlated with the local characteristics of the signal, and such filters are unable to exploit this correlation to reduce the noise. We propose a new nonlinear, space-variant filtering algorithm which smooths jagged edges without blurring them, and smooths out abrupt intensity changes in monotone areas. Edge sharpness is preserved because near edges the filtering of the signal is negligible. Consequently, in-band noise is not reduced, but the well-known masking effect reduces the visibility of this in-band noise. The algorithm is only slightly more complex to implement than simple linear filtering. We present examples of processed images and SNR figures to demonstrate that a significant improvement in subjective and objective quality is achieved.

Direct-Sequence Spread Spectrum with DPSK Modulation and Diversity for Indoor Wireless Communications

Direct-sequence spread spectrum with differential phase shift-keying (DPSK) modulation and code-division multiple-access is a promising approach for wireless communications in an indoor environment, which is characterized in this paper by a Rayleigh-fading multipath channel. In this study, we consider two specific channel models having different path-delay distributions and average path power profiles. A star configuration, in which each user exercises average power control in transmitting to a central station, is the basic communication unit, which could be one cell in a cellular hierarchy. We obtain the performance of a single link between a user and its receiver in the central station, and consider two types of diversity, selection diversity and predetection combining to exploit the multipath. A similar system with coherent PSK (CPSK) modulation has been studied previously for one of the channel models considered here. For the same channel model, we show that the irreducible error probability with selection diversity is about half an order of magnitude higher when DPSK is used instead of CPSK. With predetection combining, the performance improves significantly in comparison with selection diversity as the diversity order increases. DPSK modulation with predetection combining is akin to coherent PSK with optimal maximal-ratio combining, but is simpler to implement. The performance with selection diversity for a second channel model, which is based on measurements in an office building, is not significantly different. This indicates that the spreadspectrum approach is rather robust to the path-delay distribution and average path-power profile.

Image coding using vector quantization

An image is partitioned into cells of pxp pixels. Each cell is regarded as a vector of dimension p2and is encoded by searching through a codebook for a nearest matching representative vector. A binary word identifying the selected representative vector is assigned as the codeword to describe the original cell. The decoder uses this codeword to address a codebook. Each entry of the codebook contains a full precision digital representation of one of the N representative vectors. The codebook design is based on a clustering technique for vector quantizer design preceded by a classification of training cells into edge or shade cells. Results for coding rates from 0.5 to 1.5 bits/pixel are discussed. Vector quantization appears to be a powerful and promising technique for image coding.

Fast search algorithms for vector quantization and pattern matching

A fundamental computational task that arises in several areas of signal processing is pattern matching, where a given test pattern is compared with a large set of stored templates, to find the best match that minimizes a given measure of dissimilarity. Three different geometrically-oriented methods are proposed for substantially reducing the computational complexity of the search process by reducing the number of multiplies in exchange for additional low complexity operations and, in two of the methods, additional memory for storing precomputed tables.

Vector quantization for compression of multichannel ECG

A scheme is proposed which is based on vector quantization (VQ) for the data-compression of multichannel ECG waveforms. N-channel ECG is first coded using m-AZTEC, a new, multichannel extension of the AZTEC algorithm. As in AZTEC, the waveform is approximated using only lines and slopes; however, in m-AZTEC, the N channels are coded simultaneously into a sequence of N+1 dimensional vectors, thus exploiting the correlation that exists across channels in the AZTEC duration parameter. Classified VQ (CVQ) of the m-AZTEC output is next performed to exploit the correlation in the other AZTEC parameter, namely, the value parameter. CVQ preserves the waveform morphology by treating the lines and slopes as two perceptually distinct classes. Both m-AZTEC and CVQ provide data compression, and their performance improves as the number of channels increases.<<ETX>>

The role of technology in telecom expansion in India

It is not viable to expand the telecom network in India substantially at the prevalent level of per-line investment. However, systems based on new technologies, many developed in India, promise to more than halve the investment required. This article looks at the telecom scenario, the new technologies, the Indian products based on these technologies, and the cost reductions they promise. The provision of widespread Internet service with low access tariff is an important aspect of the new approach.

Image vector quantization with a perceptually-based cell classifier

Vector quantization (VQ) has made it possible to utilize perceptually meaningful techniques for direct space-domain image coding. A simple 2 or 3 way classified codebook approach [2,3] allocates the perceptually important edges with more resolution than the easily encoded monotone regions of an image. In this paper, we introduce a major extension of the classification approach to include edge orientation and location, thereby exploiting an important feature of the human visual mechanism. In particular, each 4 × 4 block of pixels is classified into one of 31 classes for the case of 16 dimensional VQ. The encoding and codebook design complexity is significantly reduced, allowing us to use large codebooks designed from a large database of training images. We present images encoded at 0.7 and 0.8 bits per pixel using this scheme with 16- dimensional vectors. Only a small fraction of one bit per pixel is needed to code the monotone regions of an image; the rest of the bitrate is used to achieve a high level of edge integrity.

Two-sided filters for frame-based prediction

A linear prediction model, based on a two-sided predictor which predicts on the basis of past and future samples within a frame, is presented. The linear prediction model may be applied wherever frame-based prediction is used. A stable synthesis procedure is derived by casting the prediction equation as a cyclic convolution in the time domain. When the filter order is the maximum possible, the synthesis filter is shown to have a frequency response proportional to the squared magnitude of the DFT of the frame. A symmetric two-sided predictor which has only half the number of coefficients to be coded as compared to a one-sided predictor of the same order is described. Two-sided prediction showed at least 5-dB improvement in prediction gain over one-sided prediction in simulations on speech data. >

WiFiRe: rural area broadband access using the WiFi PHY and a multisector TDD MAC

The needs of Indian rural telecom, and the economics of currently available broadband access technologies, motivate a new system for rural broadband access, which we call WiFiRe (WiFi rural extension). The system leverages the widely available, and highly cost-reduced, WiFi chipsets. We, however, retain only the PHY from these chipsets and propose a single-channel, multisector, TDD MAC using directional antennas. The proposed WiFiRe MAC is similar to the WiMAX MAC in several respects. In this article we motivate our approach, describe the system architecture and the MAC, analyse the spatial reuse, and then, using a simple scheduler, provide an assessment of the voice and data capacity of a WiFiRe system