Ramamurti Chandramouli

Adaptive quantization and fast error-resilient entropy coding for image transmission

There has been an outburst of research in image and video compression for transmission over noisy channels. Channel matched source quantizer design has gained prominence. Further, the presence of variable-length codes in compression standards like the JPEG and the MPEG has made the problem more interesting. Error-resilient entropy coding (EREC) has emerged as a new and effective method to combat catastrophic loss in the received signal due to burst and random errors. We propose a new channel-matched adaptive quantizer for JPEG image compression. A slow, frequency-nonselective Rayleigh fading channel model is assumed. The optimal quantizer that matches the human visibility threshold and the channel bit-error rate is derived. Further, a new fast error-resilient entropy code (FEREC) that exploits the statistics of the JPEG compressed data is proposed. The proposed FEREC algorithm is shown to be almost twice as fast as EREC in encoding the data, and hence the error resilience capability is also observed to be significantly better. On average, a 5% decrease in the number of significantly corrupted received image blocks is observed with FEREC. Up to a 2-dB improvement in the peak signal-to-noise ratio of the received image is also achieved.

A generalized sequential sign detector for binary hypothesis testing

It is known that for fixed error probabilities sequential signal detection based on the sequential probability ratio test (SPRT) is optimum in terms of the average number of signal samples for detection. But, often suboptimal detectors like the sequential sign detector are preferred over the optimal SPRT. When the additive noise statistic is independent and identically distributed (i.i.d.), the sign detector is preferred for its simplicity and nonparametric properties. However, in many practical applications such as the usage of high speed sampling devices the noise is correlated. A generalized sequential sign detector for detecting binary signals in stationary, first-order Markov dependent noise is studied. Under the i.i.d. assumptions, this reduces to the usual sequential sign detector. The optimal decision thresholds and the average sample number for the test to terminate are derived. Numerical results are given to show that the proposed detector exploits the correlation in the noise and hence results in quicker detection. The method can also be extended to Mth order Markov dependence by converting it to a first-order dependence in an extended state space.

Computing the bivariate Gaussian probability integral

In signal processing applications, it is often required to compute the integral of the bivariate Gaussian probability density function (PDF) over the four quadrants. When the mean of the random variables are nonzero, computing the closed form solution to these integrals with the usual techniques of integration is infeasible. Many numerical solutions have been proposed; however, the accuracy of these solutions depends on various constraints. In this work, we derive the closed form solution to this problem using the characteristic function method. The solution is derived in terms of the well-known confluent hypergeometric function. When the mean of the random variables is zero, the solution is shown to reduce to a known result for the value of the integral over the first quadrant. The solution is implementable in software packages such as MAPLE.

Joint optimization of quantization and on-line channel estimation for low bit-rate video transmission

Optimal quantization and channel estimation are one among the main issues in low bit-rate video transmission over time varying noisy channels. Previous approaches to these issues were mainly based on quantizers optimized for parametric channel models and pilot symbol aided techniques for channel identification. However, this optimality may not hold when the randomly varying channel behavior deviates from these models. Also, the cost involved in terms of delay could be large for pilot symbol based channel estimation. We propose a new empirically optimized channel matched quantizer and a stochastic learning algorithm that estimates and tracks the channel with minimal additional delay and overhead. Performance analysis of the algorithm shows that the new adaptive quantizer results in a better quality video. The learning algorithm converges very fast.

Quantization for robust sequential M-ary signal detection

In this paper, a M-ary sequential detector based on the quantized received signal samples is proposed and analyzed for amplitude-modulated signals. The problem is modeled as M-ary hypothesis testing. An L level optimal quantization algorithm based on the quantiles of the received signal is presented. The proposed quantizer detector is robust to the changes in the channel parameters. For an average decision error probability equal to 10/sup -4/, 4.6 dB of transmitted signal power is saved by using the proposed quantizer detector when compared to the fixed sample size detector.

Error resilient coding for JPEG image transmission over wireless fading channels

In this paper, a new fast error resilient entropy coder (FEREC) for robust image transmission over wireless fading channels is proposed. A slow, frequency non-selective Rayleigh fading channel model is used. The proposed FEREC algorithm is observed to be almost twice as fast as EREC in encoding the data and hence the error resilience capability is also significantly better. Up to 2 dB improvement in the peak signal to noise ratio of the received image is achieved when compared to EREC.

Computation of lower and upper bounds for switching activity: a unified approach

Accurate switching activity estimation is crucial for power budgeting. It is impractical to obtain an accurate estimate by simulating the circuit for all possible inputs. An alternate approach would be to compute tight bounds for the switching activity. In this paper, we propose a non-simulative method to compute bounds for switching activity at the logic level. First, we show that the switching activity can be modeled as the Bayesian distance for an abstract two class problem. The computation of the upper and lower bounds for the switching activity is unified in to a single function, /spl psi/(/spl alpha/,p,/spl rho/), where /spl alpha/ is a parameter, /spl rho/ is the temporal correlation factor and p is the signal probability. The constraints on /spl alpha/ for /spl psi/(/spl alpha/,p,/spl rho/) to be tight upper and lower bounds are derived. The proposed approach computes bounds for individual gate switching. Experimental results are obtained by taking spatial and temporal correlations into account. The computations are simple and fast.

Empirical channel matched quantizer design and UEP for robust image transmission

Summary form only given. Channel matched quantization for image transmission over time varying channels reduces the effects of channel errors. The presence of variable length codes in compression standards like the JPEG cause error propagation due to bit errors. Unequal error protection (UEP) schemes have emerged as an effective method to combat catastrophic loss in the received signal due to burst and random errors. An empirical channel matched quantizer design algorithm that jointly optimizes the distortion due to quantization-channel noise and a new efficient UEP scheme for image transmission are proposed. The baseline JPEG encoder is used to compress the 8-bit gray level images before transmission. A slow frequency non-selective Rayleigh fading channel is considered in this study. A quantization table optimized for human visual quality is used for very low channel bit error rates. For higher bit error rates, the quantization table is matched to the channel conditions by multiplying its entries by the optimal quantization multiplication factor, M/sup */ such that the average number of received image blocks in error is minimized. M/sup */ is computed for each bit error rate ranging from 10/sup -4/ to 10/sup -1/ through empirical modeling of the trade-off between the quantization and the channel noise. In order to enhance the performance of the proposed system, a new UEP scheme that limits the error propagation due to variable length encoding is used. This scheme works by packing the output bits of the JPEG coder into slots of fixed size.

Rate control for a video coder using learning automata

In this paper, a rate controller for a H.261 based video encoder is proposed. The rate controller adaptively chooses the optimal channel matched quantizer using a stochastic learning automaton. The automaton learns the channel characteristics based on a one bit feedback from the decoder. The rate control algorithm is shown to converge to the optimal choice of the quantizer very quickly for various channel bit error probabilities and for different video sequences. The adaptation can be achieved in real-time. The peak signal to noise ratio of the received video signal is seen to be better using the proposed approach.