Adversarial attacks perturb images such that a deep neural network produces incorrect classification results. A promising approach to defend against adversarial attacks on natural multi-object scenes is to impose a context-consistency check, wherein, if the detected objects are not consistent with an appropriately defined context, then an attack is suspected. Stronger attacks are needed to fool such context-aware detectors. We present the first approach for generating context-consistent adversarial attacks that can evade the context-consistency check of black-box object detectors operating on complex, natural scenes. Unlike many black-box attacks that perform repeated attempts and open themselves to detection, we assume a “zero-query” setting, where the attacker has no knowledge of the classification decisions of the victim system. First, we derive multiple attack plans that assign incorrect labels to victim objects in a context-consistent manner. Then we design and use a novel data structure that we call the perturbation success probability matrix, which enables us to filter the attack plans and choose the one most likely to succeed. This final attack plan is implemented using a perturbation-bounded adversarial attack algorithm. We compare our zero-query attack against a few-query scheme that repeatedly checks if the victim system is fooled. We also compare against state-of-the-art context-agnostic attacks. Against a context-aware defense, the fooling rate of our zero-query approach is significantly higher than context-agnostic approaches and higher than that achievable with up to three rounds of the fewquery scheme.

We consider the problem of privacy-preserving data aggregation in a star network topology, i.e., several untrusting participants connected to a single aggregator. We require that the participants do not discover each other's data, and the service provider remains oblivious to each participant's individual contribution. Furthermore, the final result is to be published in a differentially private manner, i.e., the result should not reveal the contribution of any single participant to a (possibly external) adversary who knows the contributions of all other participants. In other words, we require a secure multiparty computation protocol that also incorporates a differentially private mechanism. Previous solutions have resorted to caveats such as postulating a trusted dealer to distribute keys to the participants, or introducing additional entities to withhold the decryption key from the aggregator, or relaxing the star topology by allowing pairwise communication amongst the participants. In this paper, we show how to obtain a noisy (differentially private) aggregation result using Shamir secret sharing and additively homomorphic encryption without these mitigating assumptions. More importantly, while we assume semi-honest participants, we allow the aggregator to be stronger than semi-honest, specifically in the sense that he can try to reduce the noise in the differentially private result.

Viewpoint-invariant object matching is challenging due to image distortions caused by several factors such as rotation, translation, illumination, cropping and occlusion. We propose a compact, global image descriptor for Manhattan scenes that captures relative locations and strengths of edges along vanishing directions. To construct the descriptor, an edge map is determined per vanishing point, capturing the edge strengths over a range of angles measured at the vanishing point. For matching, descriptors from two scenes are compared across multiple candidate scales and displacements. The matching performance is refined by comparing edge shapes at the local maxima of the scale-displacement plots. The proposed descriptor matching algorithm achieves an equal error rate of 7% for the Zurich Buildings Database, indicating significant gains in discriminative ability over other global descriptors that rely on aggregate image statistics but do not exploit the underlying scene geometry.

We consider the problem of extracting descriptors that represent visually salient portions of a video sequence. Most state-of-the-art schemes generate video descriptors by extracting features, e.g., SIFT or SURF or other keypoint-based features, from individual video frames. This approach is wasteful in scenarios that impose constraints on storage, communication overhead and on the allowable computational complexity for video querying. More importantly, the descriptors obtained by this approach generally do not provide semantic clues about the video content. In this paper, we investigate new feature-agnostic approaches for efficient retrieval of similar video content. We evaluate the efficiency and accuracy of retrieval when k-means clustering is applied to image features extracted from video frames. We also propose a new approach in which the extraction of compact video descriptors is cast as a Non-negative Matrix Factorization (NMF) problem. Initial experiments on video-based matching suggest that compact descriptors obtained via low-rank matrix factorization improve discriminability and robustness to parameter selection compared to k-means clustering.

Traditional rate-distortion theory is focused on how to best encode a signal using as few bits as possible and incurring as low a distortion as possible. However, very often, the goal of transmission is to extract specific information from the signal at the receiving end, and the distortion should be measured on that extracted information. In this paper we examine the problem of encoding signals such that sufficient information is preserved about their pair wise distances. For that goal, we consider randomized embeddings as an encoding mechanism and provide a framework to analyze their performance. We also propose the recently developed universal quantized embeddings as a solution to that problem and experimentally demonstrate that, in image retrieval experiments, universal embedding can achieve up to 25% rate reduction over the state of the art.

Secure fingerprint authentication via encrypted-domain processing imposes constraints on the underlying feature extraction method: Firstly, it requires fixed-length feature vectors to be amenable to computing distances or correlations. Secondly, extra information must be stored in the clear so that the fingerprints can be aligned prior to feature extraction and secure comparison. These constraints potentially restrict the flexibility, increase computational complexity, and even reduce the security of the scheme. We desire feature vectors suitable for encrypted-domain matching while being free of the above constraints. To this end, a local neighborhood is defined around certain detected minutiae points, and features are extracted based on relative locations of close minutia points, local ridge texture and local ridge orientation. The locality of the features provides robustness to rotation and translation. Feature vectors are compared using operations that can be performed using secure primitives. The process of computing the matching scores - genuine or impostor - implicitly yields the best alignment without needing to store unencrypted side information at the access control device. The scheme achieves an Equal Error Rate of 1.46% on a proprietary database and 7.86% on the FVC2002 public database.

The problem in which one of three pairwise interacting parties is required to securely compute a function of the inputs held by the other two, when one party may arbitrarily deviate from the computation protocol (active behavioral model), is studied. An information-theoretic characterization of unconditionally secure computation protocols under the active behavioral model is provided. A protocol for Hamming distance computation is provided and shown to be unconditionally secure under both active and passive behavioral models using the information-theoretic characterization. The difference between the notions of security under the active and passive behavioral models is illustrated by examining a protocol for computing quadratic and Hamming distances that is secure under the passive model, but is insecure under the active model.

In an increasing number of image and video processing problems, cryptographic techniques are used to enforce content access control, identity verification and authentication, and privacy protection. The combination of cryptography and signal processing is an exciting emerging field. This introductory paper gives an overview of approaches and challenges that exist in applying cryptographic primitives to important image and video processing problems, including (partial) content encryption, secure face recognition, and secure biometrics. This paper aims to help the community in appreciating the utility and challenges of cryptographic techniques in image and video processing.

We are specifically concerned with scenarios in which multimedia data is stored once on the server and the same data is queried by multiple parties. We propose a framework for privacy preserving querying, in which encryption is performed only once, and the ciphertexts are stored on a database server. Rather than using public-key homomorphic cryptosystems, the parties querying the database first derive an “attribute” from their query signal. They can decrypt the server's ciphertext only if their attribute satisfies a specified mathematical condition. This query-specific decryption capability makes attribute-based cryptography a vital addition to the secure signal processor's toolkit. We give an example of a construction for privacy preserving querying, in which a client can privately retrieve an image from the server if attribute vectors extracted from the server's and client's images are close enough in Euclidean distance.

We propose an attribute-based cryptosystem in which decryption is conditional on the distance between attributes. Alice constructs a cipher text that consists of an encrypted message and a hidden attribute vector. Bob is able to decrypt Alice's message if and only if his attribute vector is within a specified maximum distance from Alice's attribute vector. We provide constructions for Euclidean and Hamming distances. The cryptosystem has advantages for privacy preserving querying. In particular, all parties can broadcast their respective cipher texts or store them on a database server. Then, a client -- not necessarily belonging to the original set of parties -- can independently and privately query the database server for cipher texts whose attributes are within some small distance from its own attribute. We describe an application of this cryptosystem in which a customer obtains recommendations from other customers of a movie rental company in a privacy-preserving manner.

Randomized embeddings of scale-invariant image features are proposed for retrieval of object-specific meta data in an augmented reality application. The method extracts scale invariant features from a query image, computes a small number of quantized random projections of these features, and sends them to a database server. The server performs a nearest neighbor search in the space of the random projections and returns meta-data corresponding to the query image. Prior work has shown that binary embeddings of image features enable efficient image retrieval. This paper generalizes the prior art by characterizing the tradeoff between the number of random projections and the number of bits used to represent each projection. The theoretical results suggest a bit allocation scheme under a total bit rate constraint: It is often advisable to spend bits on a small number of finely quantized random measurements rather than on a large number of coarsely quantized random measurements. This theoretical result is corroborated via experimental study of the above tradeoff using the ZuBuD database. The proposed scheme achieves a retrieval accuracy up to 94% while requiring the mobile device to transmit only 2.5 kB to the database server, a significant improvement over 1-bit quantization schemes reported in prior art.

We present a theoretical framework for the analysis of privacy and security trade-offs in secure biometric authentication systems. We use this framework to conduct a comparative information-theoretic analysis of two biometric systems that are based on linear error correction codes, namely fuzzy commitment and secure sketches. We derive upper bounds for the probability of false rejection$(P_{FR})$and false acceptance$(P_{FA})$for these systems. We use mutual information to quantify the information leaked about a user's biometric identity, in the scenario where one or multiple biometric enrollments of the user are fully or partially compromised. We also quantify the probability of successful attack$(P_{SA})$based on the compromised information. Our analysis reveals that fuzzy commitment and secure sketch systems have identical$P_{FR}$,$P_{FA}$,$P_{SA}$, and information leakage, but secure sketch systems have lower storage requirements. We analyze both single-factor (keyless) and two-factor (key-based) variants of secure biometrics, and consider the most general scenarios in which a single user may provide noisy biometric enrollments at several access control devices, some of which may be subsequently compromised by an attacker. Our analysis highlights the revocability and reusability properties of key-based systems and exposes a subtle design trade-off between reducing information leakage from compromised systems and preventing successful attacks on systems whose data have not been compromised.

Alice possesses a sample of private data from which she wishes to obtain some probabilistic inference. Bob possesses Hidden Markov Models (HMMs) for this purpose, but he wants the model parameters to remain private. This paper develops a framework that enables Alice and Bob to collaboratively compute the so-called forward algorithm for HMMs while satisfying their privacy constraints. This is achieved using a public-key additively homomorphic cryptosystem. Our framework is asymmetric in the sense that a larger computational overhead is incurred by Bob who has higher computational resources at his disposal, compared with Alice who has limited computing resources. Practical issues such as the encryption of probabilities and the effect of finite precision on the accuracy of probabilistic inference are considered. The protocol is implemented in software and used for secure keyword recognition.

A distributed coding algorithm is presented for compression of wavelet-transformed data. Data structures based on zerotrees are exploited for efficient compression of the significance map of wavelet coefficients. The coefficients are scanned in two stages, with a significance pass and refinement pass, similar to the SPIHT algorithm. The bits resulting from these passes are Slepian-Wolf coded using an LDPC syndrome code selected from a bank of available codes. A key realization is that, for each bitplane of the wavelet coefficients, the significance pass of the source data can be synchronized with that of the side information. This allows distributed compression of the significance pass. This is substantially different from previous mixed approaches in which the refinement pass was Slepian-Wolf coded, but the significance pass was coded independently. Rate-distortion results are presented for images from the ALOS AVNIR-2 multispectral dataset and compared against those obtained with SPIHT and JPEG2000.

This paper describes a new technique for embedding a message within structured shapes. It is desired that any changes in the shape owing to the embedded message are invisible to a casual observer but detectable by a specialized decoder. The message embedding algorithm represents shape outlines as a set of cubic Bezier curves and straight line segments. By slightly perturbing the Bezier curves, a single shape can spawn a library of similar-looking shapes each corresponding to a unique message. This library is efficiently stored using Adaptively Sampled Distance Fields (ADFs) which also facilitate rendering of the modified shapes at the desired resolution and fidelity. Given any modified shape, a forensic detector applies Procrustes analysis to determine the embedded message. Results of an extensive subjective test confirm that the shape modifications are indeed unobtrusive. Further, to test the recovery of the message bits in noisy physical environments, a text document is put through a print-photocopy-scan process. Message recovery is found to be stable even after multiple rounds of photocopying.

This paper describes our recent analysis on the security and privacy of biometric feature vectors obtained from fingerprint minutiae. A large number of contiguous regions (cuboids) are selected at random in the minutiae space, and several new features are extracted from the minutiae inside each such cuboid. Specifically, the features are extracted from the average minutia coordinate within a cuboid, the standard deviation of the minutiae coordinates, and the aggregate wall distance, i.e., the sum of distance of each minutia from the boundary of the cuboids. In terms of matching performance on a public database, the feature vectors provide an equal error rate of 3 % even if the imposter is allowed to use the same local patches as the genuine user. Performance within a secure biometrics framework is evaluated by applying an LDPC code to the feature vectors and storing only the syndrome at the access control device, for use in authentication. The paper concludes with a discussion on methods to analyze security and privacy of biometric systems that use such local-aggregate-based feature vectors in a secure biometric recognition framework. This discussion highlights security attacks via template injection, spoofing, and cancelability compromises and also considers the difficulty of privacy attacks via template inversion.

Alice and Bob possess sequences x and y respectively and would like to compute the ℓ1distance, namely || x - y ||1under privacy and communication constraints. The privacy constraint requires that Alice and Bob do not reveal their data to each other. The communication constraint requires that they accomplish the secure distance calculation with a small number of protocol transmissions and key exchanges. This paper describes and analyzes a privacy-preserving approximation protocol for the ℓ1distance that keeps the communication overhead manageable by performing a Johnson-Lindethe ℓ1distance that keeps the communication overhead manageable by performing a Johnson-Lindenstrauss embedding into the ℓ2space. Then, it performs secure two-party computation of ℓ2distances using Paillier homomorphic encryption. The protocol is implemented for private querying of face images, while maintaining a low communication overhead between the queryingnstrauss embedding into the ℓ2space. Then, it performs secure two-party computation of ℓ2distances using Paillier homomorphic encryption. The protocol is implemented for private querying of face images, while maintaining a low communication overhead between the querying party and a remote database of face feature vectors.

Most distributed source coding schemes involve the application of a channel code to the signal and transmission of the resulting syndromes. For low-complexity encoding with superior compression performance, graph-based channel codes such as LDPC codes are used to generate the syndromes. The encoder performs simple XOR operations, while the decoder uses belief propagation (BP) decoding to recover the signal of interest using the syndromes and some correlated side information. We consider parallelization of BP decoding on general-purpose multi-core CPUs. The motivation is to make BP decoding fast enough for realtime applications. We consider three different BP decoding algorithms: Sum-Product BP, Min-Sum BP and Algorithm E. The speedup obtained by parallelizing these algorithms is examined along with the tradeoff against decoding performance. Parallelization is achieved by dividing the received syndrome vectors among different cores, and by using vector operations to simultaneously process multiple check nodes in each core. While Min-Sum BP has intermediate decoding complexity, a “vectorized” version of Min-Sum BP performs nearly as fast as the much simpler Algorithm E with significantly fewer decoding errors. Our experiments indicate that, for the best compromise between speed and performance, the decoder should use Min-Sum BP when the side information is of good quality and Sum-Product BP otherwise.

As increasing amounts of sensitive personal information finds its way into data repositories, it is important to develop analysis mechanisms that can derive aggregate information from these repositories without revealing information about individual data instances. Though the differential privacy model provides a framework to analyze such mechanisms for databases belonging to a single party, this framework has not yet been considered in a multi-party setting. In this paper, we propose a privacy-preserving protocol for composing a differentially private aggregate classifier using classifiers trained locally by separate mutually untrusting parties. The protocol allows these parties to interact with an untrusted curator to construct additive shares of a perturbed aggregate classifier. We also present a detailed theoretical analysis containing a proof of differential privacy of the perturbed aggregate classifier and a bound on the excess risk introduced by the perturbation. We verify the bound with an experimental evaluation on a real dataset.

This paper investigates the application of lossy distributed source coding to high resolution multispectral images. The choice of distributed source coding is motivated by the need for very low encoding complexity on space and airborne platforms. The data consists of red, blue, green and infra-red channels and is compressed in an asymmetric Wyner-Ziv setting. One image channel is compressed using traditional JPEG and transmitted to the ground station where it is available as side information for Wyner-Ziv coding of the other channels. Encoding is accomplished by quantizing the image data, applying a Low-Density Parity Check code to the remaining three image channels, and transmitting the resulting syndromes. At the ground station, the image data is recovered from the syndromes by exploiting the correlation in the frequency spectrum of the band being decoded and the JPEG-decoded side information band. In experiments with real uncompressed images obtained by a satellite, the rate-distortion performance is found to be vastly superior to JPEG compression of individual image channels and rivals that of JPEG2000 at much lower encoding complexity.

This paper describes a method for hiding data inside printed text documents that is resilient to print/scan and photocopying operations. Using the principle of channel coding with side information, the embedder inserts a message into a text document while treating the content of the document as known interference. The data is embedded by making small changes to text characters before the document is printed. Using a simple correlation-based detector in conjunction with an error correction code, the hidden data can be extracted from a photocopy of the printed document. By enhancing the detector with an optical character recognition algorithm, the embedded data can be extracted even after multiple rounds of photocopying. Results from subjective tests show that the changes made by the embedding algorithm, while perceptible, are not obtrusive to a lay reader.

Alice and Bob possess sequences xnand ynrespectively and would like to compute d(xn, yn) where d(· , ·) is a distortion measure. However, Alice and Bob do not trust each other and do not wish to reveal their data to each other. This paper describes and analyzes a protocol that uses homomorphic encryption for secure calculation of some special distortion functions without revealing xnand yn. The resulting distortion result is also in encrypted form. Two variants of the protocol are presented, one for the Hamming distance between binary vectors, and the other for squared error distortion between integer vectors. An application of the protocol for private biometric authentication is described in which Bob interacts with a remote encrypted fingerprint database (Alice) to achieve access control without revealing his own identity.

In the above titled paper (ibid., vol. 18, no. 10, pp. 1347-1360, Oct 08), Fig. 4 was printed incorrectly. The correct figure is presented here.

We present an information-theoretically secure biometric storage system using graph-based error correcting codes in a Slepian-Wolf coding framework. Our architecture is motivated by the noisy nature of personal biometrics and the requirement to provide security without storing the true biometric at the device. The principal difficulty is that real biometric signals, such as fingerprints, do not obey the i.i.d. or ergodic statistics that are required for the underlying typicality properties in the Slepian-Wolf coding framework. To meet this challenge, we propose to transform the biometric data into binary feature vectors that are i.i.d. Bernoulli(0.5), independent across different users, and related within the same user through a BSC-p channel with small p< 0.5. Since this is a standard channel model for LDPC codes, the feature vectors are now suitable for LDPC syndrome coding. The syndromes serve as secure biometrics for access control. Experiments on a fingerprint database demonstrate that the system is information-theoretically secure, and achieves very low false accept rates and low false reject rates.

This paper proposes a scheme called Systematic Lossy Error Protection (SLEP) for robust transmission of video signals over packet erasure channels. The systematic portion of the transmission consists of a conventionally encoded video bit stream which is transmitted without channel coding. An additional bit stream generated by Wyner–Ziv encoding of the video signal is transmitted for error resilience. In the event of packet loss, this supplementary bit stream is decoded and allows the recovery of a coarsely quantized video signal, which is displayed in lieu of the lost portions of the primary video signal. The quantization mismatch results in a small, controlled loss in picture quality, but a drastic reduction in picture quality is avoided. An implementation of the SLEP system using the state-of-the-art H.264/AVC standard codec is described. Specifically, H.264/AVC redundant slices are used in conjunction with Reed–Solomon coding to generate the Wyner–Ziv bit stream. The received video quality is modeled as a function of the bit rates of the primary and redundant descriptions and the error resilience bit rate. The model is used to optimize the video quality delivered by SLEP. Via theoretical analysis and experimental simulation, it is shown that SLEP provides a flexible tradeoff between error resilience and decoded picture quality. By allowing the quality to degrade gracefully over a wider range of packet loss rates, SLEP mitigates the precipitous drop in picture quality suffered by traditional FEC-based systems.

We consider a first-order Markov source, which is predictively encoded using a DPCM-style encoder. The quantized compressed prediction residual is transmitted over an erasure channel. Additionally, a Wyner-Ziv encoded version of the prediction residual is transmitted in order to provide error resilience. When the symbols from the first transmission are erased by the channel, this second description is decoded, and limits the maximum distortion that can occur. Since the quantization step size used in the second description is, in general, larger than that used in the main transmission, error protection is lossy. Using high-rate quantization theory, we derive expressions for the rate and the end-to-end distortion incurred by this system. We show that, compared to conventional lossless forward error correction, this lossy error protection scheme is robust in the sense that it allows the received signal quality to degrade gracefully when the erasure probability increases

This paper proposes a scheme for low-delay robust transmission of video signals over packet erasure channels. In applications such as video conferencing, the permissible delay between encoding and playback may be too low to allow retransmission or channel coding approaches which require buffering several video packets. For such a scenario, we present a scheme that provides error robustness using redundant video descriptions applied to pertinent portions of the video signal. In the H.264/AVC specification, this can be efficiently implemented using redundant slices and flexible macroblock ordering (FMO). We describe a model that determines the bit rate of the redundant descriptions such that the expected distortion at the decoder is minimized. Across all the video test sequences used, the average video quality delivered by the proposed scheme is 3.7 dB higher than decoder-based error concealment, and 1.2 dB higher than encoder-based loss-aware rate-distortion optimization.

This paper proposes systematic lossy error protection of video waveforms using multiple embedded Wyner-Ziv video descriptions. A video signal transmitted over an error-prone channel, without channel coding, constitutes the systematic portion of the transmission. Error protection is achieved by additionally transmitting two or more bitstreams generated by Wyner-Ziv coding of the video signal. The Wyner-Ziv bitstreams contain multiple embedded coarsely quantized descriptions of the original video signal. In the event of channel errors, the Wyner-Ziv descriptions are decoded, thereby limiting the maximum distortion that can occur. The available Wyner-Ziv bit-rate is allocated among the multiple embedded descriptions, using the observation that the visibility of slice losses and quantization artifacts depends on the employed coding structure. Experimental results show that the trade-off between error-resilience and residual quantization distortion can be better exploited using multiple embedded Wyner-Ziv descriptions, compared to earlier results with a single Wyner-Ziv bitstream. The system delivers gracefully deteriorating video quality without requiring a layered video compression scheme in the systematic portion of the transmission.

Distributed coding is a new paradigm for video compression, based on Slepian and Wolf's and Wyner and Ziv's information-theoretic results from the 1970s. This paper reviews the recent development of practical distributed video coding schemes. Wyner-Ziv coding, i.e., lossy compression with receiver side information, enables low-complexity video encoding where the bulk of the computation is shifted to the decoder. Since the interframe dependence of the video sequence is exploited only at the decoder, an intraframe encoder can be combined with an interframe decoder. The rate-distortion performance is superior to conventional intraframe coding, but there is still a gap relative to conventional motion-compensated interframe coding. Wyner-Ziv coding is naturally robust against transmission errors and can be used for joint source-channel coding. A Wyner-Ziv MPEG encoder that protects the video waveform rather than the compressed bit stream achieves graceful degradation under deteriorating channel conditions without a layered signal representation.

In the current interframe video compression systems, the encoder performs predictive coding to exploit the similarities of successive frames. The Wyner-Ziv theorem on source coding with side information available only at the decoder suggests that an asymmetric video codec, where individual frames are encoded separately, but decoded conditionally (given temporally adjacent frames) could achieve similar efficiency. In the previous work we propose a Wyner-Ziv coding scheme for motion video that uses intraframe encoding instead of interframe decoding. In this paper we improve on our Wyner-Ziv video codec by sending hash codewords of the current frame to aid the decoder in accurately estimating the motion. This allows us to implement a low-delay system where only the previous reconstructed frame is used to generate the side information of a current frame. Simulation results show significant gains above conventional DCT-based intraframe coding. The Wyner-Ziv video codec with hash-based motion compensation at the receiver enables low-complexity encoding while achieving high compression efficiency.

We present a practical scheme for error-resilient digital video broadcasting, using the Wyner-Ziv coding paradigm. We apply the general framework of systematic lossy source-channel coding to generate a supplementary bitstream that can correct transmission errors in the decoded video waveform up to a certain residual distortion. The systematic portion consists of a conventional MPEG-2 bitstream, which is transmitted over the error-prone channel without forward error correction. The supplementary bitstream is a low rate representation of the transmitted video sequence generated using Wyner-Ziv encoding. We use the received error-prone MPEG-2 prediction error signal as side information to decode the Wyner-Ziv bits. The decoder combines the error-prone side information and the Wyner-Ziv description to yield an improved decoded video signal. Wc describe a system that uses an embedded Wyner-Ziv codec to achieve graceful quality degradation without the need for a layered representation of the video waveform.

In current interframe video compression systems, the encoder performs predictive coding to exploit the similarities of successive frames. The Wyner-Ziv Theorem on source coding with side information available only at the decoder suggests that an asymmetric video codec, where individual frames are encoded separately, but decoded conditionally (given temporally adjacent frames) could achieve similar efficiency. We propose a transformdomain Wyner-Ziv coding scheme for motion video that uses intraframe encoding, but interframe decoding. In this system, the transform coefficients of a Wyner-Ziv frame are encoded independently using a scalar quantizer and turbo coder. The decoder uses previously reconstructed frames to generate side information to conditionally decode the Wyner-Ziv frames. Simulation results show significant gains above DCT-based intraframe coding and improvements over the pixel-domain Wyner-Ziv video coder.

We present a novel scheme for error-resilient digital video broadcasting,using the Wyner-Ziv coding paradigm. We apply the general framework of systematic lossy source-channel coding to generate a supplementary bitstream that can correct transmission errors in the decoded video waveform up to a certain residual distortion. The systematic portion consists of a conventional MPEG-coded bitstream, which is transmitted over the error-prone channel without forward error correction.The supplementary bitstream is a low rate representation of the transmitted video sequence generated using Wyner-Ziv encoding. We use the conventionally decoded error-concealed MPEG video sequence as side information to decode the Wyner-Ziv bits. The decoder combines the error-prone side information and the Wyner-Ziv description to yield an improved decoded video signal. Our results indicate that, over a large range of channel error probabilities, this scheme yields superior video quality when compared with traditional forward error correction techniques employed in digital video broadcasting.

Two separate applications of Wyner-Ziv coding of motion video coding are considered. The Wyner-Ziv theorem on source coding with side information available only at the decoder suggests that an asymmetric video codec, where individual frames are encoded separately, but decoded conditionally could achieve efficiency comparable to current interframe video compression systems. First results on the Wyner-Ziv coding scheme for motion video are reported that use interframe encoding, but interframe decoding. Secondly, Wyner-Ziv coding for the transmission of compressed video over an error-prone channel is applied. This coding scheme is used to generate a supplementary bit stream, which contains a coarsely quantized representation of the transmitted video signal. Using the conventionally decoded, error-concealed video signal as side information to decode the Wyner-Ziv bits, the transmission errors in the decoded video waveform are corrected up to a certain residual distortion, significantly improving the visual quality of the decoded video.

A novel scheme for error-resilient digital video broadcasting, using Wyner-Ziv coding, is presented in this paper. We apply the general framework of systematic lossy source-channel coding to generate a supplementary bitstream that can correct transmission errors in the decoded video waveform up to a certain residual distortion. The systematic portion consists of a conventional MPEG-coded bitstream, which is transmitted over the error-prone channel without any forward error correction. The supplementary bitstream is a low rate representation of the transmitted video sequence generated using Wyner-Ziv encoding. We use the conventionally decoded error-concealed MPEG video sequence as side information to decode the Wyner-Ziv bits. The decoder combines the error-prone side information and the Wyner-Ziv description to yield an improved decoded video signal. We show how this scheme can be used to build a system which achieves graceful degradation without the need of a layered representation of the video waveform.

An approach for filling-in blocks of missing data in wireless image transmission is presented. When compression algorithms such as JPEG are used as part of the wireless transmission process, images are first tiled into blocks of 8 x 8 pixels. When such images are transmitted over fading channels, the effects of noise can destroy entire blocks of the image. Instead of using common retransmission query protocols, we aim to reconstruct the lost data using correlation between the lost block and its neighbors. If the lost block contained structure, it is reconstructed using an image inpainting algorithm, while texture synthesis is used for the textured blocks. The switch between the two schemes is done in a fully automatic fashion based on the surrounding available blocks. The performance of this method is tested for various images and combinations of lost blocks. The viability of this method for image compression, in association with lossy JPEG, is also discussed.

An approach for filling-in blocks of missing data in wireless image transmission is presented in this paper. When compression algorithms such as JPEG are used as part of the wireless transmission process, images are first tiled into blocks of 8/spl times/8 pixels. When such images are transmitted over fading channels, the effects of noise can kill entire blocks of the image. Instead of using common retransmission query protocols, we aim to reconstruct the lost data using correlation between the lost block and its neighbors. If the lost block contained structure, it is reconstructed using an image inpainting algorithm, while texture synthesis is used for the textured blocks. The switch between the two schemes is done in a fully automatic fashion based on the surrounding available blocks. The performance of this method is tested for various images and combinations of lost blocks. The viability of this method for image compression, in association with lossy JPEG, is also discussed.

A fast scheme for wavelet-domain interpolation of lost image blocks in wireless image transmission is presented in this paper. In the transmission of block-coded images, fading in wireless channels and congestion in packet-switched networks can cause entire blocks to be lost. Instead of using retransmission query protocols, we reconstruct the lost block in the wavelet-domain using the correlation between the lost block and its neighbors. The algorithm first uses simple thresholding to determine the presence or absence of edges in the lost block. This is followed by an interpolation scheme, designed to minimize the blockiness effect, while preserving the edges or texture in the interior of the block. The interpolation scheme minimizes the square of the error between the border coefficients of the lost block and those of its neighbors, at each transform scale. The performance of the algorithm on standard test images, its low computational overhead at the decoder, and its performance vis-a-vis other reconstruction schemes, is discussed.

The compression of elevation data is studied. The performance of JPEG-LS, the new international ISO/ITU standard for lossless and near-lossless (controlled-lossy) still-image compression, is investigated both for data from the USGS digital elevation model (DEM) database and the navy-provided digital terrain model (DTM) data. Using JPEG-LS has the advantage of working with a standard algorithm. Moreover, in contrast with algorithms like the popular JPEG-lossy standard, this algorithm permits the completely lossless compression of the data as well as a controlled lossy mode where a sharp upper bound on the elevation error is selected by the user. All these are achieved at a very low computational complexity. In addition to these algorithmic advantages, they show that JPEG-LS achieves significantly better compression results than those obtained with other (nonstandard) algorithms previously investigated for the compression of elevation data. The results here reported suggest that JPEG-LS can immediately be adopted for the compression of elevation data for a number of applications.