nils-herrmann/clean_scirepeval huggingface dataset:
Perturbation-enhanced feature correlation filter for robust iris recognition

Visual pattern of human iris provides rich texture information for personal identification. However, it is challenging to match intra-class iris images with large variations in applications. This study proposes a perturbation-enhanced feature correlation filter (PFCF) for robust iris matching. PFCF is developed based on quad-phase minimum average correlation energy filter, but it has two significant improvements. First, PFCF is performed on Gabor filtered iris images to encode both local and global features. On the one hand, Gabor images can enhance the local details of iris texture. On the other hand, correlation filters describe the regional appearance information and measure the global similarity between iris images efficiently. Secondly, artificially perturbed iris images are generated to model intra-class variations. Also, a set of additional correlation filters are developed accordingly as the gallery templates. The decision is determined by the fusion result of multiple correlation filters. Therefore PFCF not only takes the advantages of Gabor images and correlation filters but also enlarges the amount of enrolled templates for robust iris matching. Extensive experiments on three challenging iris image databases demonstrate that the proposed method outperforms the state-of-the-art methods according to its robustness against deformation, rotation, occlusion, blurring and illumination changes in iris images.
Automatic 3D face modeling from video

In this paper, we develop an efficient technique for fully automatic recovery of accurate 3D face shape from videos captured by a low cost camera. The method is designed to work with a short video containing a face rotating from frontal view to profile view. The whole approach consists of three components. First, automatic initialization is performed in the first frame with approximately frontal face. Then, to handle the case of low quality image captured by low cost camera, the 2D feature matching, head poses and underlying 3D face shape are estimated and refined iteratively in an efficient way based on image sequence segmentation. Finally, to take advantage of the sparse structure of the proposed algorithm, sparse bundle adjustment technique is further employed to speed up the computation. We demonstrate the accuracy and robustness of the algorithm using a set of experiments
On compressing encrypted data

When it is desired to transmit redundant data over an insecure and bandwidth-constrained channel, it is customary to first compress the data and then encrypt it. In this paper, we investigate the novelty of reversing the order of these steps, i.e., first encrypting and then compressing, without compromising either the compression efficiency or the information-theoretic security. Although counter-intuitive, we show surprisingly that, through the use of coding with side information principles, this reversal of order is indeed possible in some settings of interest without loss of either optimal coding efficiency or perfect secrecy. We show that in certain scenarios our scheme requires no more randomness in the encryption key than the conventional system where compression precedes encryption. In addition to proving the theoretical feasibility of this reversal of operations, we also describe a system which implements compression of encrypted data.
Optimal multiple description subband coding

Multiple description coding refers to encoding an information source into multiple bitstreams, in a way that various levels of tradeoff between decoding quality and channel protection can be supported. This paper addresses the design of two-channel orthonormal filter banks to optimize the redundancy rate-distortion performance of multiple description subband coding. The problem is formulated in a Lagrangian optimization framework, and is solved directly in the frequency domain to produce the optimal filter responses. By varying the Lagrangian parameter, our design obtains all points on the information redundancy rate-distortion curve for Gaussian wide sense stationary input processes. Application of the proposed algorithm to the popular AR(1) model yields very interesting and insightful results. Comparisons with popularly deployed filter banks reveal large gaps from the theoretical performance bound.
Frame-bufferless sum-rate constrained video encoding using feedback

We investigate the design of a frame-bufferless and low-latency video communication system, where the encoder has no access to any previous frames and only one use of feedback communication from the decoder to the encoder is allowed in encoding each frame. This strict requirement on encoder complexity and system latency is of critical importance in designing low complexity realtime video communication systems, where the encoder is limited in storage and computing resources and the use of the feedback channel must not incur accumulated receiver playback delay. We propose a simple online statistical model that captures the time-varying video process and develop a general framework that explores how to best utilize the feedback and distributed source coding mechanisms to minimize the overall communication rate. Our analysis and experimental results validate the efficiency of the proposed system and highlight its potential in practical system design.
Robust image transmission over energy-constrained time-varying channels using multiresolution joint source-channel coding

We explore joint source-channel coding (JSCC) for time-varying channels using a multiresolution framework for both source coding and transmission via novel multiresolution modulation constellations. We consider the problem of still image transmission over time-varying channels with the channel state information (CSI) available at (1) receiver only and (2) both transmitter and receiver being informed about the state of the channel, and we quantify the effect of CSI availability on the performance. Our source model is based on the wavelet image decomposition, which generates a collection of subbands modeled by the family of generalized Gaussian distributions. We describe an algorithm that jointly optimizes the design of the multiresolution source codebook, the multiresolution constellation, and the decoding strategy of optimally matching the source resolution and signal constellation resolution "trees" in accordance with the time-varying channel and show how this leads to improved performance over existing methods. The real-time operation needs only table lookups. Our results based on a wavelet image representation show that our multiresolution-based optimized system attains gains on the order of 2 dB in the reconstructed image quality over single-resolution systems using channel optimized source coding.
Space-frequency quantization for a space-varying wavelet packet image coder

We introduce a new image coding algorithm which exploits the idea of space-varying wavelet packets, where the best filter bank representation is chosen from a large library. The filter bank tree representations in the library are free to vary in structure over different segments of the image, and a fast search algorithm is given. In addition we employ the idea of space-frequency quantization, which is a rate-distortion optimized extension of the zero-tree wavelet coder of Shapiro to wavelet packets. The coder thus adaptively chooses the representation to suit the image and adaptively chooses the quantization to suit the representation. We present coding results that confirm the excellent performance of the scheme.
Distributed Fountain Codes for Networked Storage

We investigate the problem of constructing fountain codes for distributed storage in sensor networks. Specifically, we assume that there are n storage nodes with limited memory and k < n data nodes generating the data by sensing the environment. We want a data collector who can appear anywhere in the network, to query any k + epsi storage nodes and be able to retrieve almost all the data packets. We demonstrate how it is possible to solve this problem by using a specific kind of fountain code that requires only linear communication and decoding complexity. Further, for a grid topology, we propose a randomized algorithm that constructs the fountain code over a network using only geographical knowledge and local decisions. A key step in the analysis of our algorithm is a novel result concerning random walks on finite grids with traps
Joint thresholding and quantizer selection for decoder-compatible baseline JPEG

This paper introduces a novel, image-adaptive, encoding scheme for the baseline JPEG standard. In particular, coefficient thresholding, JPEG quantization matrix (Q-matrix) optimization, and adaptive Huffman entropy-coding are jointly performed to maximize coded still-image quality within the constraints of the baseline JPEG syntax. Adaptive JPEG coding has been addressed in earlier works: by Ramchandran and Vetterli (see ), where fast rate-distortion (R-D) optimal coefficient thresholding was described, and by Wu and Gersho (see Proc.  1993) and Hung and Meng (1991), where R-D optimized Q-matrix selection was performed. By formulating an algorithm which optimizes these two operations jointly, we have obtained performance comparable to more complex, "state-of-the-art" coding schemes: for the "Lenna" image at 1 bpp, our algorithm has achieved a PSNR of 39.6 dB. This result represents a gain of 1.7 dB over JPEG with a customized Huffman entropy coder, and even slightly exceeds the published performance of Shapiro's (see , p.3445-3462, December 1993) wavelet-based scheme. Furthermore, with the choice of appropriate visually-based error metrics, noticeable subjective improvement has been achieved as well.
Robust Predictive Quantization: Analysis and Design Via Convex Optimization

Predictive quantization is a simple and effective method for encoding slowly-varying signals that is widely used in speech and audio coding. It has been known qualitatively that leaving correlation in the encoded samples can lead to improved estimation at the decoder when encoded samples are subject to erasure. However, performance estimation in this case has required Monte Carlo simulation. Provided here is a novel method for efficiently computing the mean-squared error performance of a predictive quantization system with erasures via a convex optimization with linear matrix inequality constraints. The method is based on jump linear system modeling and applies to any autoregressive moving average (ARMA) signal source and any erasure channel described by an aperiodic and irreducible Markov chain. In addition to this quantification for a given encoder filter, a method is presented to design the encoder filter to minimize the reconstruction error. Optimization of the encoder filter is a nonconvex problem, but we are able to parameterize with a single scalar a set of encoder filters that yield low MSE. The design method reduces the prediction gain in the filter, leaving the redundancy in the signal for robustness. This illuminates the basic tradeoff between compression and robustness.
Achievable Rates for Channels With Deletions and Insertions

This paper considers a binary channel with deletions and insertions, where each input bit is transformed in one of the following ways: it is deleted with probability d, or an extra bit is added after it with probability i, or it is transmitted unmodified with probability 1-d-i. A computable lower bound on the capacity of this channel is derived. The transformation of the input sequence by the channel may be viewed in terms of runs as follows: some runs of the input sequence get shorter/longer, some runs get deleted, and some new runs are added. It is difficult for the decoder to synchronize the channel output sequence to the transmitted codeword mainly due to deleted runs and new inserted runs. The main idea is a mutual information decomposition in terms of the rate achieved by a suboptimal decoder that determines the positions of the deleted and inserted runs in addition to decoding the transmitted codeword. The mutual information between the channel input and output sequences is expressed as the sum of the rate achieved by this decoder and the rate loss due to its suboptimality. Obtaining computable lower bounds on each of these quantities yields a lower bound on the capacity. The bounds proposed in this paper provide the first characterization of achievable rates for channels with general insertions, and for channels with both deletions and insertions. For the special case of the deletion channel, the proposed bound improves on the previous best lower bound for deletion probabilities up to 0.3.
SRAM supply voltage scaling: A reliability perspective

SRAM leakage power is a significan fraction of the total power consumption on a chip. Various system level techniques have been proposed to reduce this leakage-power by reducing (scaling) the supply voltage. SRAM supply voltage scaling reduces the leakage-power, but it increases stored-data failure rate due to commonly known failure mechanisms, for example, soft-errors. This work studies SRAM leakage-power reduction using system level design techniques, with a data-reliability constraint. A statistical or probabilistic setup is used to model failure mechanisms like soft-errors or process-variations, and error-probability is used as a metric for reliability. Error models which combine various SRAM cell failure mechanisms are developed. In a probabilistic setup, the bit-error probability increases due to supply voltage reduction, but it can be compensated by suitable choices of error-correction code and data-refresh (scrubbing) rate. The trade-offs between leakage-power, supply voltage reduction, data-refresh rate, error-correction code, and decoding error probability are studied. The leakage-power ¿ including redundancy overhead, coding power, and data-refresh power ¿ is set as the cost-function and an error-probability target is set as the constraint. The cost-function is minimized subject to the constraint, over the choices of data-refresh rate, error-correction code, and supply voltage. Using this optimization procedure, simulation results and circuit-level leakage-power reduction estimates are presented.
On Compression of Encrypted Video

We consider video sequences that have been encrypted uncompressed. Since encryption masks the source, traditional data compression algorithms are rendered ineffective. However, it has been shown that through the use of distributed source-coding techniques, the compression of encrypted data is in fact possible. This means that it is possible to reduce data size without requiring that the data be compressed prior to encryption. Indeed, under some reasonable conditions, neither security nor compression efficiency need be sacrificed when compression is performed on the encrypted data (Johnson et al., 2004). In this paper we develop an algorithm for the practical lossless compression of encrypted gray scale video. Our method is based on considering the temporal correlations in the video. This move to temporal dependence builds on our previous work on memoryless sources, and one- and two-dimensional Markov sources. For comparison, a motion-compensated lossless video encoder can compress each unencrypted frame of the standard "Foreman" test video sequence by about 57%. Our algorithm can compress the same frames, after encryption, by about 33%
Explicit codes uniformly reducing repair bandwidth in distributed storage

A distributed storage setting is considered where a file of size B is to be stored across n storage nodes. A data collector should be able to reconstruct the entire data by downloading the symbols stored in any k nodes. When a node fails, it is replaced by a new node by downloading data from some of the existing nodes. The amount of download is termed as repair bandwidth. One way to implement such a system is to store one fragment of an (n, k) MDS code in each node, in which case the repair bandwidth is B. Since repair of a failed node consumes network bandwidth, codes reducing repair bandwidth are of great interest. Most of the recent work in this area focuses on reducing the repair bandwidth of a set of k nodes which store the data in uncoded form, while the reduction in the repair bandwidth of the remaining nodes is only marginal. In this paper, we present an explicit code which reduces the repair bandwidth for all the nodes to approximately B/2. To the best of our knowledge, this is the first explicit code which reduces the repair bandwidth of all the nodes for all feasible values of the system parameters.
Fast sparse 2-D DFT computation using sparse-graph alias codes

We present a novel algorithm, named the 2D-FFAST (Two-dimensional Fast Fourier Aliasing-based Sparse Transform), to compute a sparse 2D-Discrete Fourier Transform (2D-DFT) featuring both low sample and computational complexity. The proposed algorithm is based on diverse concepts from signal processing (sub-sampling and aliasing), coding theory (sparse-graph codes) and number theory (Chinese-remainder-theorem) and generalizes the 1D-FFAST algorithm recently proposed by Pawar and Ramchandran to the 2D setting. Concretely, our proposed 2D-FFAST algorithm computes a k-sparse 2D-DFT, with a uniformly random support, of size N = Nx × Ny using O(k) noiseless spatial-domain measurements in O(k log k) computational time. Our results are attractive when the sparsity is sub-linear with respect to the signal dimension, that is, when k → ∞ and k/N → 0. For the case when the spatial-domain measurements are corrupted by additive noise, our 2D-FFAST framework extends to a noise-robust version of computing a 2D-DFT using O(k log3 N) measurements in sub-linear time of O(k log4 N). Empirically, we show that the 2D-FFAST can compute a k = 3509 sparse 2D-DFT of a 508 × 508-size phantom image using only 4.75k measurements. We also empirically evaluate the 2D-FFAST algorithm on a real-world magnetic resonance brain image using a total of 60.18% of Fourier measurements to provide an almost instant reconstruction with SNR=4.5 dB. This provides empirical evidence that the 2D-FFAST architecture is applicable to a wider class of input signals than analyzed theoretically in the paper.
Explicit and optimal exact-regenerating codes for the minimum-bandwidth point in distributed storage

In the distributed storage setting that we consider, data is stored across n nodes in the network such that the data can be recovered by connecting to any subset of k nodes. Additionally, one can repair a failed node by connecting to any d nodes while downloading β units of data from each. Dimakis et al. show that the repair bandwidth dβ can be considerably reduced if each node stores slightly more than the minimum required and characterize the tradeoff between the amount of storage per node and the repair bandwidth.
Information-Theoretic Bounds on Target Recognition Performance Based on Degraded Image Data

This paper derives bounds on the performance of statistical object recognition systems, wherein an image of a target is observed by a remote sensor. Detection and recognition problems are modeled as composite hypothesis testing problems involving nuisance parameters. We develop information-theoretic performance bounds on target recognition based on statistical models for sensors and data, and examine conditions under which these bounds are tight. In particular, we examine the validity of asymptotic approximations to probability of error in such imaging problems. Problems involving Gaussian, Poisson, and multiplicative noise, and random pixel deletions are considered, as well as least-favorable Gaussian clutter. A sixth application involving compressed sensor image data is considered in some detail. This study provides a systematic and computationally attractive framework for analytically characterizing target recognition performance under complicated, non-Gaussian models and optimizing system parameters.
Rate-constrained distributed distance testing and its applications

We investigate a practical approach to solving one instantiation of a distributed hypothesis testing problem under severe rate constraints that shows up in a wide variety of applications such as camera calibration, biometric authentication and video hashing: given two distributed continuous-valued random sources, determine if they satisfy a certain Euclidean distance criterion. We show a way to convert the problem from continuous-valued to binary-valued using binarized random projections and obtain rate savings by applying a linear syndrome code. In finding visual correspondences, our approach uses just 49% of the rate of scalar quantization to achieve the same level of retrieval performance. To perform video hashing, our approach requires only a hash rate of 0.0142 bpp to identify corresponding groups of pictures correctly.
PRISM: an uplink-friendly multimedia coding paradigm

In this work, we present PRISM (Power-efficient Robust high-compression Syndrome-based Multimedia coding), a new video coding paradigm based on the principles of distributed source coding. Incurring the low encoding complexity of intra-frame (still-image) video coding, PRISM approaches the high compression efficiency of full-motion inter-frame video coding, while simultaneously offering natural robustness to the drift problem and channel loss in direct contrast to conventional video coding architectures (MPEG and H.26L). These traits make it well-matched to uplink-rich applications such as multimedia over wireless networks, wireless video and sensor cameras, etc.
Efficient Private Information Retrieval Over Unsynchronized Databases

Web search histories can reveal detailed and sensitive information about people. Private information retrieval (PIR) tackles this potential privacy violation by allowing users to retrieve the wth record of a database without revealing w to the server. However, most known PIR schemes are either very inefficient (and therefore unlikely to gain traction in a practical sense) or reliant on some restrictive assumptions. In this paper, we consider an efficient class of schemes called multi-server PIR. Multi-server PIR assumes that the client communicates with multiple, non-colluding servers, each possessing an identical copy of the database. Significant prior work has gone towards relaxing the anti-collusion assumption, but the literature does not address the assumption that servers store perfectly-synchronized databases. This seems implausible, especially if servers are not meant to collude. We propose the first multi-server PIR scheme to return the desired record even when servers' databases are not perfectly synchronized. Our scheme asymptotically has the same computational and communication complexity as state-of-the-art PIR schemes for synchronized databases; this comes at the expense of probabilistic success and two rounds of communication (most existing schemes require only one). Additionally, this approach efficiently processes multiple concurrent PIR queries.