nils-herrmann/clean_scirepeval huggingface dataset:
Rate-distortion-optimal subband coding without perfect-reconstruction constraints

We investigate the design of subband coders without the traditional perfect-reconstruction constraint on the filters. The coder uses scalar quantizers, and its filters and bit allocation are designed to optimize a rate-distortion criterion. Using convexity analysis, we show that optimality can be achieved using filterbanks that are the cascade of a (paraunitary) principal component filterbank for the input spectral process and a set of pre and postfilters surrounding each quantizer. Analytical expressions for the pre and postfilters are then derived. An algorithm for computing the globally optimal filters and bit allocation is given. We also develop closed-form solutions for the special case of two-channel coders under an exponential rate-distortion model. Finally, we investigate a constrained-length version of the filter design problem, which is applicable to practical coding scenarios. While the optimal filterbanks are nearly perfect-reconstruction at high rates, we demonstrate an apparently surprising advantage of optimal FIR filterbanks; they significantly outperform optimal perfect-reconstruction FIR filterbanks at all bit rates.
Joint source channel coding with hybrid FEC/ARQ for buffer constrained video transmission

We propose an automatic repeat request (ARQ)/forward error correction (FEC) scheme for synchronous transmission of video over a binary symmetric constant rate channel. The approach consists of jointly allocating source and channel rates to video blocks from a given admissible set subject to the buffer or equivalently end-end delay constraints. The channel codes used are the popular class of powerful FEC codes known as rate-compatible punctured convolutional (RCPC) codes. The method used involves independent coding of the video units and optimization of the end-to-end expected delivered video quality. The existence of a return channel is assumed through which the decoder informs the encoder about the success/failure of the transmission. In the event of a failure, incremental parity information is sent to the decoder for correcting errors and a reallocation performed at the encoder. The simulations done point out the efficacy of the proposed scheme.
On decoder-latency versus performance tradeoffs in differential predictive coding

Theoretical analysis of differential predictive coding (DPC) has almost exclusively focused on scalar quantizers and the high-rate regime for tractability reasons. As a result, the role of noncausal decoding in improving the quality has been largely ignored in the literature. In this work we conduct a rigorous performance analysis of DPC-based schemes under a simple independent, vector-Gaussian, AR-1 source model and large-block (as opposed to high-rate) asymptotics. This analysis reveals that noncausal decoding can offer a significant relative improvement in the mean squared error (by as much as 3 dB) at medium to low rates (0.1-0.5 bit per sample) for sources having strong temporal correlation. Furthermore, most of this relative improvement can be attained with a modest decoder-latency. At very high and very low rates, the gains are negligible.
Data exchange problem with helpers

In this paper we construct a deterministic polynomial time algorithm for the problem where a set of users is interested in gaining access to a common file, but where each has only partial knowledge of the file. We further assume the existence of another set of terminals in the system, called helpers, who are not interested in the common file, but who are willing to help the users. Given that the collective information of all the terminals is sufficient to allow recovery of the entire file, the goal is to minimize the (weighted) sum of bits that these terminals need to exchange over a noiseless public channel in order achieve this goal. Based on established connections to the multi-terminal secrecy problem, our algorithm also implies a polynomial-time method for constructing the largest shared secret key in the presence of an eavesdropper. We consider the following side-information settings: (i) side-information in the form of uncoded packets of the file, where the terminals' side-information consists of subsets of the file packets; (ii) side-information in the form of linearly correlated packets, where the terminals have access to linear combinations of the file packets; and (iii) the general setting where the the terminals' side-information has an arbitrary (i.i.d.) correlation structure. We provide a polynomial-time algorithm (in the number of terminals) that finds the optimal rate allocations for these terminals, and then determines an explicit optimal transmission scheme for cases (i) and (ii).
Toward Compression of Encrypted Images and Video Sequences

We present a framework for compressing encrypted media, such as images and videos. Encryption masks the source, rendering traditional compression algorithms ineffective. By conceiving of the problem as one of distributed source coding, it has been shown in prior work that encrypted data are as compressible as unencrypted data. However, there are two major challenges to realize these theoretical results. The first is the development of models that capture the underlying statistical structure and are compatible with our framework. The second is that since the source is masked by encryption, the compressor does not know what rate to target. We tackle these issues in this paper. We first develop statistical models for images before extending it to videos, where our techniques really gain traction. As an illustration, we compare our results to a state-of-the-art motion-compensated lossless video encoder that requires unencrypted video input. The latter compresses each unencrypted frame of the ldquoForemanrdquo test sequence by 59% on average. In comparison, our proof-of-concept implementation, working on encrypted data, compresses the same sequence by 33%. Next, we develop and present an adaptive protocol for universal compression and show that it converges to the entropy rate. Finally, we demonstrate a complete implementation for encrypted video.
Secure communication using an untrusted relay via sources and channels

Confidential communication aided by a relay without security clearance is studied. General strategies and outer bounds are derived for the problem of secret communication and secret key generation when correlated observations at all terminals are available. In a variation of the problem, it is assumed that the quality of the channel to the relay is known only to the relay. If the throughput-maximizing strategy is used according to the relay's claimed channel quality, the question is: what should the relay claim about the channel in order to maximize its eavesdropping capabilities? We propose a strategy that Alice and Bob may agree on in order to suppress any leakage of confidential communication between the source and the receiver.
Optimal supports for linear predictive models

Linear predictive models seek to optimally extract information about a sample of a signal based on some subset of its causal past. Very little work has been done in investigating the importance and choice of this subset (support) in the prediction process. The paper addresses the problem of finding the optimal support for use by a linear predictive model. The authors derive a general result relating the distortion incurred in predicting a sample of a stationary signal based on a causal support in terms of the Wiener coefficients of a larger support and the autocorrelation matrix. Based on the above result, they derive an algorithm which optimally reduces the size of the support by one at each stage. The algorithm is tested on the Barbara image for image estimation and on the football image sequence for pel-recursive motion compensation and is shown to outperform (by large margins in some cases) conventionally chosen supports.<<ETX>>
'Distributed' signal processing: new opportunities and challenges

We are at a novel crossroads in technology where we are witnessing the confluence of computing, communicating and networking. A number of exciting applications are both driving and being driven by this confluence, including low-power sensor networks, large-scale ad hoc wireless networks, and wireless multimedia transmission. Many of these applications demand a move away from classical centralized architectures and algorithms towards more decentralized and distributed ones. Signal processing plays a key role in this revolution- not in isolation but rather as a pivotal interdisciplinary systems component, intimately integrated with communications, information theory, coding theory, and networking protocols. Sensor networks represent a particularly rich applications base. We would provide a snapshot of the sensor network related activities in a number of research groups at Berkeley. Motivated by the communications and computational constraints imposed by large-scale low-power sensor networks, we would describe some of our signal processing centric research including: (i) distributed sampling; (ii) distributed source coding; (iii) distributed estimation; and (iv) robust transmission. We would highlight the key foundational role played by multi-user information theory, particularly the so-called area of side-information coding for both source coding (compression) and channel coding (transmission). A deeper look reveals a beautiful functional duality between source and channel coding with side-information. This unexpectedly unifies a host of seemingly unrelated problem areas like distributed compression, digital watermarking, multimedia transmission over packet-error networks, and seamless digital upgrade of analog TV. Finally, as a microcosm of the expressive power of interdisciplinary thinking, we would describe a novel video compression paradigm dubbed PRISM (power-efficient, robust, hlh-compression, syndrome-based multimedia coding). PRISM's architecture, in stark contrast to that driving current video codecs like MPEG, allows for a novel shifting of the computational complexity from the encoder to the decoder, making it ideally suited for "uplink" transmission scenarios in wireless multimedia and surveillance applications.
Spread spectrum interference suppression using adaptive time-frequency tilings

Interference suppression in spread spectrum communication systems is often essential for achieving maximum system performance. Existing interference suppression methods do not perform well for most types of nonstationary interference. We first consider interference suppression schemes based on adaptive orthogonal time-frequency decompositions, such as wavelet packet and arbitrary dyadic time-frequency tilings. These methods often reduce interference substantially, but their performance can vary dramatically with minor changes in interference characteristics such as the center frequency. To circumvent these drawbacks, we propose a multiple overdetermined tiling (MODT) with an accompanying blind interference excision scheme which appears very promising for mitigating time-frequency-concentrated interference. Simulations with narrowband, impulsive, and simultaneous impulsive and narrowband interference compare the performance of the various methods and illustrate the promise of approaches based on multiple overdetermined tilings.
Multi-Antenna Interference Cancellation Techniques for Cognitive Radio Applications

This paper presents a practical method for using multi-antenna radios to cancel interference in cognitive radio systems. Under this method, secondary radio transmitters use beamforming techniques to find antenna weights that place nulls at the primary receivers, and secondary radio receivers use adaptive techniques to decode in the presence of interference from primary users. As an example, we show how this scheme can be leveraged to effectively reuse the uplink band of a cellular network. However, estimating the channel responses, without causing interference and without requiring significant modifications to legacy systems, is a challenging problem. We provide an iterative method for accurate channel estimation in frequency division duplexed networks, where the uplink is independent of the downlink.
Secret communication using sources and channels

Alice wants to send Bob a secret message in the presence of an eavesdropper Eve. Alice has two resources available: a noisy broadcast channel and source observations correlated with observations at Bob and Eve. We consider two cases, the first in which the source observations and channel are independent and the second in which they can depend on each other. For the independent case, we consider a strategy that exploits the sources and channels separately and show this separation strategy is optimal for a class of sources and channels that includes a Gaussian example. For the case in which the sources and channels depend on each other, this separation strategy does not work, and an optimal strategy has yet to be established. For this setting, we improve on the best known upper bound to the secret message capacity.
Random Multiresolution Representations for Arbitrary Sensor Network Graphs

We propose a distributed multiresolution representation of sensor network data so that large-scale summaries are readily available by querying a small fraction of sensor nodes, anywhere in the network, and small-scale details are available by querying a larger number of sensors, locally in the region of interest. A global querier (such as a mobile collector or unmanned aerial vehicle) can obtain a lossy to lossless representation of the network data, according to the desired resolution. A local querier (such as a sensor node) can also obtain either large-scale trends or local details, by querying its immediate neighborhood. We want the encoding to be robust to arbitrary, even time-varying, wireless communication connectivity graphs. Thus we want to avoid cluster heads or deterministic hierarchies that are not robust to single points of failure. We propose a randomized encoding which enables both robustness, and distributed computation that does not require long distance coordination or awareness of network connectivity at individual sensors. Our distributed encoding algorithm operates on local neighborhoods of the communication graph
A comparative study of DCT and wavelet based coding

We study the performance difference of the discrete cosine transform (DCT) and the wavelet transform for both image and video coding, while comparing other aspects of the coding system on an equal footing. Based on the state-of-the-art coding techniques, we point out that, for still images, wavelet outperforms DCT with a coding gain of about one dB in PSNR. For video coding, the advantage of wavelet schemes is less obvious. We believe that image and video compression algorithms should be addressed from the overall system viewpoint. Quantization, entropy coding, and paying attention to details are more important than spending all the efforts on optimizing the transform.
View Synthesis for Robust Distributed Video Compression in Wireless Camera Networks

We propose a method for delivering error-resilient video from wireless camera networks in a distributed fashion over lossy channels. Our scheme is based on distributed source coding that exploits inter-view correlation among cameras with overlapping views. The main focus in this work is on robustness which is imminently needed in a wireless setting. The proposed approach has low encoding complexity, is robust while satisfying tight latency constraints, and requires no inter-camera communication. Our system is built on and is a multi-camera extension of PRISM[1], an earlier proposed single-camera distributed video compression system. Decoder motion search, a key attribute of single-camera PRISM, is extended to the multi-view setting by using estimated scene depth information when it is available. In particular, dense stereo correspondence and view synthesis are utilized to generate side-information. When combined with decoder motion search, our proposed method can be made insensitive to small errors in camera calibration, disparity estimation and view synthesis. In experiments over a simulated wireless channel, the proposed approach achieves up to 2.1 dB gain in PSNR over a system using H.263+ with forward error correction.
Multiple description source coding using forward error correction codes

We present an efficient multiple description (MD) source coding scheme to achieve robust communication over unreliable channels. In contrast to the popular signal processing based methods, we propose channel coding principles to correlate the descriptions, and then use this correlation for combating channel impairments. We propose a fast, nearly optimal algorithm that aims to maximize the expected quality at the receiver given the channel state and the side channel rates. Our scheme can be used in conjunction with any source coder that is scalable, and is most easily matched to coders outputting a progressive bitstream. It has applications to the transmission of audio, images, as well as delay-constrained video signals, and can also be used to achieve reliable multicast transmission over the existing Internet with the use of simple protocols. Comparisons of our scheme on standard test images to some of the existing state-of-the-art signal processing based MD methods suggest that our simple scheme outperforms them by significant margins.
Fast and robust compressive phase retrieval with sparse-graph codes

In this paper, we tackle the compressive phase retrieval problem in the presence of noise. The noisy compressive phase retrieval problem is to recover a K-sparse complex signal s ∈ ℂ<sup>n</sup>, from a set of m noisy quadratic measurements: y<sub>i</sub> = |a<sub>i</sub><sup>H</sup>s|<sup>2</sup> + w<sub>i</sub>; where a<sub>i</sub><sup>H</sup> ∈ ℂ<sup>n</sup> is the ith row of the measurement matrix A ∈ ℂ<sup>m×n</sup>, and w<sub>i</sub> is the additive noise to the ith measurement. We consider the regime where K = βn<sup>δ</sup>, δ ∈ (0; 1). We use the architecture of PhaseCode algorithm , and robustify it using two schemes: the almost-linear scheme and the sublinear scheme. We prove that with high probability, the almost-linear scheme recovers s with sample complexity<sup>1</sup> Θ(K log(n)) and computational complexity Θ(n log(n)), and the sublinear scheme recovers s with sample complexity Θ(K log<sup>3</sup>(n)) and computational complexity Θ(K log<sup>3</sup>(n)). To the best of our knowledge, this is the first scheme that achieves sublinear computational complexity for compressive phase retrieval problem. Finally, we provide simulation results that support our theoretical contributions.
Distributed coding for wireless audio sensors

Future multimedia systems will use multiple audio and video input and output streams to enhance user experience. Those multiple input streams may be captured using a network of distributed sensors and transmitted to a central location for processing. We address the problem of efficient joint compression of audio sources that are noisy filtered versions of the same audio signal. Deployed in a wireless bandwidth constrained network, communication between the sources, if any, is restricted to a bare minimum. By exploiting the correlations between the remote sources, we develop algorithms for distributed compression of these audio sources, attempting to achieve the gains predicted in theory. Our scheme shows a significant improvement in reconstructed signal quality for a given bandwidth as compared to an independent compression approach. The algorithms are based on the distributed source coding using syndromes (DISCUS) framework and incorporate the use of perceptual masks.
The MDS queue: Analysing the latency performance of erasure codes

In order to scale economically, data centers are increasingly evolving their data storage methods from the use of simple data replication to the use of more powerful erasure codes, which provide the same level of reliability as replication but at a significantly lower storage cost. In particular, it is well known that Maximum-Distance-Separable (MDS) codes, such as Reed-Solomon codes, provide the maximum storage efficiency. While the use of codes for providing improved reliability in archival storage systems, where data is less frequently accessed (or so-called “cold data”), is well understood, the role of codes in the storage of more frequently accessed and active “hot data”, where latency is the key metric, is less clear. In this paper, we study data storage systems based on MDS codes through the lens of queueing theory, and term the queueing system arising under codes as an “MDS queue.” We present insightful scheduling policies that form upper and lower bounds to its performance, and use these to obtain easily computable analytical bounds on the average latency of the MDS queue. These bounds were observed to be quite tight in the settings we simulated. We additionally derive closed-form expressions of the throughputs of these systems. Finally, we employ the framework of the MDS queue to analyse different methods of performing so-called degraded reads (reading of partial data) in distributed data storage.
Wavelet based image coding via morphological prediction of significance

In previous work, we introduced a new image representation for the field of wavelet coefficients (dubbed MRWD), based on morphological operators. This work extends the MRWD framework, by addressing the effective design of image coding algorithms. First, we design an encoder with the goal of being optimal in the operational rate-distortion sense. Second, based on the same (morphological) techniques, we design a successively refinable version of the single rate coder. Simulation results are reported.
Multiple description wavelet based image coding

We consider the problem of coding images for transmission over error-prone channels. The impairments we target are transient channel shutdowns, as would occur in a packet network when a packet is lost, or in a wireless system during a deep fade: when data is delivered it is assumed to be error-free, but some of the data may never reach the receiver. The proposed algorithms are based on a combination of multiple description scalar quantizers with techniques successfully applied to the construction of some of the most efficient subband coders. A given image is encoded into multiple independent packets of roughly equal length. When packets are lost, the quality of the approximation computed at the receiver depends only on the number of packets received, but does not depend on exactly which packets are actually received. When compared with previously reported results on the performance of robust image coders based on multiple descriptions, on standard test images, our coders attain similar PSNR values using typically about 50-60% of the bit rate required by these other state-of-the-art coders, while at the same time providing significantly more freedom in the mechanism for allocation of redundancy among descriptions.
Circulant structures and graph signal processing

Linear shift-invariant processing of graph signals rests on circulant graphs and filters. The spatial features of circulant structures also permit shift-varying operations such as sampling. Their spectral features-as described by their Graph Fourier Transform profiles-enable novel multiscale signal processing systems and methods. To extend the reach of circulant structures, we present a method to decompose an arbitrary graph or filter into a combination of circulant structures. Our decomposition is analogous to resolving a linear time-varying system into a bank of linear time-invariant systems. As an application, we perform multiscale decomposition on temperature data spanning the continental United States.