David P. Varodayan

Rate-adaptive codes for distributed source coding

Source coding with correlated decoder side information is considered. We impose the practical constraint that the encoder be unaware of even the statistical dependencies between source and side information. Two classes of rate-adaptive distributed source codes, both based on low-density parity-check (LDPC) codes, are developed and their design is studied. Specific realizations are shown to be better than alternatives of linear encoding and decoding complexity. The proposed rate-adaptive LDPC accumulate (LDPCA) codes and sum LDPC accumulate (SLDPCA) codes (of length 6336 bits) perform within 10% and 5% of the Slepian-Wolf bound in the moderate and high rate regimes, respectively.

Rate-Adaptive Distributed Source Coding using Low-Density Parity-Check Codes

Source coding with correlated decoder side infor- mation is considered. We impose the practical constraint that the encoder be unaware of even the statistical dependencies between source and side information. Two classes of rate-adaptive distributed source codes, both based on Low-Density Parity- Check (LDPC) codes, are developed and their design is studied. Specific realizations are shown to be better than alternatives of linear encoding and decoding complexity. The proposed rate- adaptive LDPC Accumulate (LDPCA) codes and Sum LDPC Accumulate (SLDPCA) codes (of length 6336 bits) perform within 10% and 5% of the Slepian-Wolf bound in the moderate and high rate regimes, respectively.

Smart meter privacy using a rechargeable battery: Minimizing the rate of information leakage

A rechargeable battery may be used to partially protect the privacy of information contained in a households electrical load profile. We represent the system as a finite state model to make tractable the computation of the rate of information leakage. Specifically, we use a trellis algorithm to estimate the mutual information rate between the batterys input and output loads. We show that stochastic battery policies can leak 26% less information than a so-called best-effort algorithm (that holds the output load constant whenever possible). We finally describe the extension of the technique to more realistic models of the battery system.

Wyner-Ziv coding of video with unsupervised motion vector learning

Distributed source coding theory has long promised a new method of encoding video that is much lower in complexity than conventional methods. In the distributed framework, the decoder is tasked with exploiting the redundancy of the video signal. Among the difficulties in realizing a practical codec has been the problem of motion estimation at the decoder. In this paper, we propose a technique for unsupervised learning of forward motion vectors during the decoding of a frame with reference to its previous reconstructed frame. The technique, described for both pixel-domain and transform-domain coding, is an instance of the expectation maximization algorithm. The performance of our transform-domain motion learning video codec improves as GOP size grows. It is better than using motion-compensated temporal interpolation by 0.5dB when GOP size is 2, and by even more when GOP size is larger. It performs within about 0.25dB of a codec that knows the motion vectors through an oracle, but is hundreds of orders of magnitude less complex than a corresponding brute-force decoder motion search approach would be.

Image Authentication Based on Distributed Source Coding

Image authentication is important in content delivery via untrusted intermediaries, such as peer-to-peer (P2P) file sharing. Many differently encoded versions of the original image might exist. On the other hand, intermediaries might tamper with the contents. Distinguishing the legitimate diversity of encodings from malicious manipulation is the challenge addressed in this paper. We develop a novel approach based on distributed source coding for the problem of backward-compatible image authentication. The key idea is to provide a Slepian-Wolf encoded quantized image projection as authentication data. This version can be correctly decoded only with the help of an authentic image as side information. Distributed source coding provides the desired robustness against legitimate encoding variations, while detecting illegitimate modification. We demonstrate false acceptance rates close to zero for authentication data sizes that are only a few percent of the compressed image size.

Region-of-interest prediction for interactively streaming regions of high resolution video

This paper investigates region-of-interest (ROI) prediction strategies for a client-server system that interactively streams regions of high resolution video. ROI prediction enables pro-active pre-fetching of select slices of encoded video from the server to allow low latency of interaction despite the delay of packets on the network. The client has a buffer of low resolution overview video frames available. We propose and study ROI prediction schemes that can take advantage of the motion information contained in these buffered frames. The system operates in two modes. In the manual mode, the user interacts actively to view select regions in each frame of video. The ROI prediction in this mode aims to reduce the distortion experienced by the viewer in his desired ROI. In the tracking mode, the user simply indicates an object to track and the system supplies an ROI trajectory without further interaction. For this mode, the prediction aims to create a smooth and stable trajectory that satisfies the user’s expectation of tracking. While the motion information enables the tracking mode, it also improves the ROI prediction in the manual mode.

Distributed Grayscale Stereo Image Coding with Unsupervised Learning of Disparity

Distributed compression is particularly attractive for stereo images since it avoids communication between cameras. Since compression performance depends on exploiting the redundancy between images, knowing the disparity is important at the decoder. Unfortunately, distributed encoders cannot calculate this disparity and communicate it. We consider the compression of grayscale stereo images, and develop an expectation maximization algorithm to perform unsupervised learning of disparity during the decoding procedure. Towards this, we devise a novel method for joint bitplane distributed source coding of grayscale images. Our experiments with both natural and synthetic 8-bit images show that the unsupervised disparity learning algorithm outperforms a system which does no disparity compensation by between 1 and more than 3 bits/pixel and performs nearly as well as a system which knows the disparity through an oracle

Image Authentication and Tampering Localization using Distributed Source Coding

Media authentication is important in content delivery via untrusted intermediaries, such as peer-to-peer (P2P) file sharing. Many differently encoded versions of a media file might exist. Our previous work applied distributed source coding to distinguish the legitimate diversity of encoded images from tampering. An authentication decoder was supplied with a Slepian-Wolf encoded lossy version of the image as authentication data. Distributed source coding provided the desired robustness against legitimate encoding variations, while detecting illegitimate modification. We augment the decoder to localize tampering in an image already deemed to be unauthentic. The localization decoder requires only incremental localization data beyond the authentication data since we use rate-adaptive distributed source codes. Both decoders perform joint bitplane decoding, rather than conditional bitplane decoding. Our results demonstrate that tampered image blocks can be identified with high probability using authentication plus localization data of only a few hundred bytes for a 512times512 image.

Optimal electric energy storage operation

Estimating the arbitrage value of storage is an important problem in power systems planning. Various studies have reported different values based numerical solutions of variations of a basic model. In this paper, we instead rely on a closed form solution for storage control. The closed form highlights the right type of forecasting that is required and allows large horizon problems to be solved. We study various scenarios and provide a simple methodology for evaluating the arbitrage value of storage.

Image Authentication Using Distributed Source Coding

We present a novel approach using distributed source coding for image authentication. The key idea is to provide a Slepian-Wolf encoded quantized image projection as authentication data. This version can be correctly decoded with the help of an authentic image as side information. Distributed source coding provides the desired robustness against legitimate variations while detecting illegitimate modification. The decoder incorporating expectation maximization algorithms can authenticate images which have undergone contrast, brightness, and affine warping adjustments. Our authentication system also offers tampering localization by using the sum-product algorithm.