nils-herrmann/clean_scirepeval huggingface dataset:
Incoherent color frames for compressive demosaicing

In this paper, we explore the notion of using frames to project sensed colors within their inherently 3D space onto a larger number of color basis vectors. In particular, we develop a new frame design, Incoherent Color Frames (ICF), which can include an arbitrary number of incoherent color vectors. An ICF frame possesses key desired properties including the ability to sparsify colors in 3D and to decorrelate color channels utilizing a spatial-frequency selective strategy. We present a low complexity algorithm for constructing ICF frames targeted for the problem of image demosaicing. Our simulation results show that when incorporating the proposed ICF within a Compressive Demosaicing (CD) framework , significant visual improvements can be achieved when compared with traditional and Compressed Sesnsing-based demosaicing solutions.
Multiresolution image compression with BSP trees and multilevel BTC

This paper presents a new multiresolution segmentation-based algorithm for image compression. High quality low bit rate image compression is achieved by recursively coding the binary space partitioning (BSP) tree representation of images with multilevel block truncation coding (BTC). Comparison with JPEG at rates below 0.25 bit/pixel shows superior performance both in terms of power signal-to-noise ratio (PSNR) and subjective image quality.
An Energy Efficient Link Layer Protocol for Power-Constrained Wireless Networks

In this paper, we develop a Reliable Energy Adept Link-layer (REAL) protocol for power-constrained networks to provide reliable data transmissions among battery-operated wireless nodes. REAL dynamically performs error recovery with respect to the overall distortions in the system and the available energy at wireless nodes. REAL employs rate-adaptive low density parity check (LDPC) codes for error recovery. We develop a theoretical model to estimate the distortion imposed by wireless channels and to compute the likelihood of successful decoding at the link-layer. Next, we present a recovery model using Markovian decision process to formulate the optimal decoding policy as a linear optimization problem. We design and implement the REAL protocol which provides system reliability with efficient energy utilization. We demonstrate experimentally that REAL achieves 5%-40% throughput and 3%-18% energy consumption improvements over channel traces with varying bit error rates (BERs) collected on 802.15.4 environment. Further, REAL shows 5-15db PSNR better video quality over various realtime video scenarios.
Video receiver based real-time estimation of channel capacity

This paper examines the problem of real-time estimation of the capacity, which is also known as the bottleneck bandwidth, of a network path using end-to-end measurements in a video streaming application. We utilize two basic packet-pair bandwidth-sampling methods and show how they can be applied in real-time to the video traffic. We then examine the performance of both sampling methods in a number of tests conducted using an MPEG-4 congestion-controlled streaming application over a Cisco network under a variety of conditions (including high link utilization scenarios). In addition, we study two simple estimation techniques, which can be applied in real-time to the collected samples, and show their performance in the same Cisco network with each of the sampling methods. We focus here on low-complexity and simple estimators that can be used in real-time by a wide range of receivers. We find that the sampling methods combined with the best estimator maintain very accurate estimates for the majority of the corresponding session in a variety of scenarios and could in fact be used by the application for congestion control in video over the Internet.
A case study of: Sender transmission reliability and complexity using network coding with multi-generation mixing

A case study of: Sender transmission reliability and complexity using network coding with Multi-generation MImprovements in emerging practical network coding methods, such as Multi-generation Mixing (MGM), can be attributed to two arguably independent factors: (1) An intrinsic factor that is due to the cooperation among intermediate (propagating) nodes in mixing traffic received through multiple routing paths. (2) An extrinsic factor that is due to the level of reliability provided by network coding from the sender toward the intermediate nodes. The vast majority of prior work has primarily focused on the intrinsic factor, while the impact of reliability of sender transmission toward the network (extrinsic factor of network coding) has not been largely investigated. In this paper we evaluate the performance of network coding with MGM in improving the reliability of sender transmission. Since MGM is a generalized approach for practical network coding, we adopt MGM for conducting this study. Using different loss models and through extensive simulations we evaluate the performance of MGM as well as traditional generation based NC (a special case of MGM) and their ability in reliably transmitting sender packets.
Optimal Linear Combination of Denoising Schemes for Efficient Removal of Image Artifacts

Different denoising schemes show dissimilar types of artifacts. For example, certain transform-based denoising schemes could introduce artifacts in smooth regions while others eliminate texture regions. Using different schemes for denoising a noisy image, we can consider the denoising results as different estimates of the image. Through linear combination of the results, we minimize the l2 norm of the error to find the optimum coefficients in a least-square-error sense. We employ the wavelet transform, contourlet transform, and adaptive 2-D Wiener filtering as our denoising schemes. Then we apply the proposed method to the denoising results of the individual schemes. This approach eliminates most of the artifacts and achieves significant improvement in the PSNR values. We also propose averaging of the denoising results as a special case of linear combination and show that it yields near-optimal performance
Hybrid Compressed Sensing of images

We consider the problem of recovering a signal/image (x) with a k-sparse representation, from hybrid (complex and real), noiseless linear samples (y) using a mixture of complex-valued sparse and real-valued dense projections within a single matrix. The proposed Hybrid Compressed Sensing (HCS) employs the complex-sparse part of the projection matrix to divide the n-dimensional signal (x) into subsets. In turn, each subset of the signal (coefficients) is mapped onto a complex sample of the measurement vector (y). Under a worst-case scenario of such sparsity-induced mapping, when the number of complex sparse measurements is sufficiently large then this mapping leads to the isolation of a significant fraction of the k non-zero coefficients into different complex measurement samples from y. Using a simple property of complex numbers (namely complex phases) one can identify the isolated non-zeros of x. After reducing the effect of the identified non-zero coefficients from the compressive samples, we utilize the real-valued dense submatrix to form a full rank system of equations to recover the signal values in the remaining indices (that are not recovered by the sparse complex projection part). We show that the proposed hybrid approach can recover a k-sparse signal (with high probability) while requiring only m ≈ 3√n/2k real measurements (where each complex sample is counted as two real measurements). We also derive expressions for the optimal mix of complex-sparse and real-dense rows within an HCS projection matrix. Further, in a practical range of sparsity ratio (k/n) suitable for images, the hybrid approach outperforms even the most complex compressed sensing frameworks (namely basis pursuit with dense Gaussian matrices). The theoretical complexity of HCS is less than the complexity of solving a full-rank system of m linear equations. In practice, the complexity can be lower than this bound.
Encoder buffer constraints for video transmission over networks with no quality-of-service guarantees

Real-time transmission of video over networks with no QoS guarantees (e.g., the Internet) is increasingly becoming an important application area in multimedia communications. One of the key challenges in this area is maintaining continuous decoding and playback at the receiver despite severe network impairments such as high packet-loss-ratios, packet-delay-variations, and unbounded roundtrip delays. A popular solution to this problem is to introduce large buffering delays at the receiver to (partially) compensate for the network impairments. This solution, however, is not a viable option for clients with limited resources and for applications requiring minimal delays. We advocate a new approach to this challenge. We derive a new set of encoder buffer constraints taking into consideration network impairments. We show how these new constraints can significantly reduce (or possibly eliminate) underflow and overflow events at the decoder while enabling the receiver to maintain its ideal decoder buffer size and buffering-delay requirements (needed for video transmission under ideal conditions). We present the results of more than 40 hours of video transmission over the (real) Internet. These results demonstrate the advantages and limitations (asymptotic behavior) of our proposed encoder-buffer constraints.
Practical compressed sensing with log-of-prime projections

In this paper, we propose a new approach for compressed sensing of integer-valued signals using prime numbers. In particular, we utilize the logarithmic values of prime numbers to construct projection matrices that are capable of significant reductions in the number of observations (m) needed for the recovery of integer-valued signals when compared to leading compressed-sensing methods. At one extreme, and under ideal conditions, the proposed Log of Prime-numbers (LoP) projection enables single-observation compressed sensing, where one sample (m = 1) can be used for the recovery of a sparse signal with N original integer samples. More importantly, we design a practical LoP projection system and a corresponding low-complexity solver that only requires m = k observations, where k is the sparsity of the signal S in some space ?. We compare the performance of the proposed LoP system with popular Basis Pursuit (BP) and Orthogonal Matching Pursuit (OMP) methods, and demonstrate the significant improvements that can be achieved by utilizing LoP projection matrices.
Cold-Start Recommendation with Provable Guarantees: A Decoupled Approach

Although the matrix completion paradigm provides an appealing solution to the collaborative filtering problem in recommendation systems, some major issues, such as data sparsity and cold-start problems, still remain open. In particular, when the rating data for a subset of users or items is entirely missing, commonly known as the  cold-start problem, the standard matrix completion methods are inapplicable due the non-uniform sampling of available ratings. In recent years, there has been considerable interest in dealing with cold-start users or items that are principally based on the idea of exploiting other sources of information to compensate for this lack of rating data. In this paper, we propose a novel and general algorithmic framework based on matrix completion that simultaneously exploits the similarity information among users and items to alleviate the cold-start problem. In contrast to existing methods, our proposed recommender algorithm, dubbed DecRec, decouples the following two aspects of the cold-start problem to effectively exploit the side information: (i) the completion of a rating sub-matrix, which is generated by excluding cold-start users/items from the original rating matrix; and (ii) the transduction of knowledge from existing ratings to cold-start items/users using side information. This crucial difference prevents the error propagation of completion and transduction, and also significantly boosts the performance when appropriate side information is incorporated. The recovery error of the proposed algorithm is analyzed theoretically and, to the best of our knowledge, this is the first algorithm that addresses the cold-start problem with provable guarantees on performance. Additionally, we also address the problem where both cold-start user and item challenges are present simultaneously. We conduct thorough experiments on real datasets that complement our theoretical results. These experiments demonstrate the effectiveness of the proposed algorithm in handling the cold-start users/items problem and mitigating data sparsity issue.
Utilizing SSR Indications for Improved Video Communication in Presence of 802.11B Residue Errors

Radio hardware used for the reception of 802.11b frames is capable of associating a signal to silence ratio (SSR) with each received frame. If a received frame is corrupted, then these SSR indications can be used to provide robust apriori estimate of the bit error rate in the packet. In many recently proposed cross-layer protocols, for transmission of video over wireless networks, recovery of information from partially corrupted packets has shown significant utility. In this paper, based on experiments with actual 802.11b error traces, we show that the channel state information (CSI) provided by the SSR indications can be used to improve the error recovery performance of an FEC scheme employed in conjunction with a cross-layer protocol. H.264 based simulation are used to establish the efficacy of the proposed work for video applications; specifically for video over 802.11b WLAN
Network Coding with Multi-Generation Mixing: Analysis and Applications for Video Communication

Network Coding (NC) is an emerging networking approach that improves overall throughput over packet networks. Meanwhile, traditional NC approaches have limited advantages under certain network conditions, such as sparse connectivity and high losses; this is especially true for real-time applications. In this paper, we propose and analyze a generalized approach of network coding, which is based on Multi-Generation Mixing (MGM). As we demonstrate in this paper, MGM-based NC improves the performance of real-time data communications under scenarios of sparse connectivity and high loss rates. Under such scenarios, practical network coding not only fail to achieve any improvements; on the contrary it may lead to performance degradations. The analytical as well as the simulation studies we present in this paper show major improvements that can be achieved in situations where practical network coding is not a viable option. In particular, we demonstrate major gains in PSNR video quality under MGM-based network coding.
Practical Network Coding for scalable video in error prone networks

In this paper we apply a generalized approach for practical Network Coding (NC) called Multi-Generation Mixing (MGM) in networks communicating scalable video contents. NC has been a viable approach of communication in packet loss networks. On the other hand NC losses are expensive. NC losses reduce the ability of receiver to decode packets, and hence may severely degrade the quality of recovered video. MGM employs the layering of scalable video streams to enhance the reliability of communication. MGM provides unequal protection for the different video layers such that the overall reliability of video communication is improved. With MGM instead of having enhancement layers just dependent on lower layers, enhancement layers support the recovery of lower layers. This is done by network encoding packets of lower layers in higher layers. Through extensive simulations, we show that MGM highly improves the quality of recovered video.
PEEC: a channel-adaptive feedback-based error

Reliable transmission is a challenging task over wireless LANs since wireless links are known to be susceptible to errors. Although the current IEEE802.11 standard ARQ error control protocol performs relatively well over channels with very low bit error rates (BERs), this performance deteriorates rapidly as the BER increases. This paper investigates the problem of reliable transmission in a contention free wireless LAN and introduces a packet embedded error control (PEEC) protocol, which employs packet-embedded parity symbols instead of ARQ-based retransmission for error recovery. Specifically, depending on receiver feedback, PEEC adaptively estimates channel conditions and administers the transmission of (data and parity) symbols within a packet. This enables successful recovery of both new data and old unrecovered data from prior transmissions. In addition to theoretically analyzing PEEC, the performance of the proposed scheme is extensively analyzed over real channel traces collected on 802.11b WLANs. We compare PEEC performance with the performance of the IEEE802.il standard ARQ protocol as well as contemporary protocols such as enhanced ARQ and the hybrid ARQ/FEC. Our analysis and experimental simulations show that PEEC outperforms all three competing protocols over a wide range of actual 802.11b WLAN collected traces. Finally, the design and implementation of PEEC using an adaptive low-density-parity-check (A-LDPC) decoder is presented.
Maximum-Likelihood Header Estimation: A Cross-Layer Methodology for Wireless Multimedia

We propose a novel cross-layer header estimation methodology that can be used by UDP-based wireless multimedia applications to estimate corrupted packet headers, thereby realizing significant throughput improvements. The proposed methodology requires only minor modifications to the protocol stack at the receiver while no modifications are needed to senders or intermediate nodes. We formulate header estimation as a problem of maximum-likelihood estimation of known parameters in noise. We derive likelihood functions for two wireless channel models, namely Markov and multifractal wavelet models. Our trace-driven video simulations at 2, 5.5 and 11 Mbps data rates of an 802.11b LAN demonstrate that significant improvements over normal UDP and UDP Lite can be achieved by employing header estimation with UDP.
Overlay and peer-to-peer multicast with network-embedded FEC

Under traditional IP multicast, application-level FEC can only be implemented on an end-to-end basis between the sender and the clients. Emerging overlay and peer-to-peer (p2p) networks open the door for new paradigms of network FEC. The deployment of FEC within these emerging networks has received very little attention (if any). In this paper, we analyze and optimize the impact of network-embedded FEC (NEF) in overlay and p2p multimedia multicast networks. Under NEF, we place FEC codecs in selected intermediate nodes of a multicast tree. The NEF codecs detect and recover lost packets within FEC blocks at earlier stages before these blocks arrive at deeper intermediate nodes or at the final leaf nodes. This approach significantly reduces the probability of receiving undecodable FEC blocks. In essence, the proposed NEF codecs work as signal regenerators in a communication system and can reconstruct most of the lost data packets without requiring retransmission. We develop an optimization algorithm for the placement of NEF codecs within random multicast trees. Our theoretical analysis and simulation results show that a relatively small number of NEF codecs placed in (sub-)optimally selected intermediate nodes of a network can improve the throughput and overall reliability dramatically.
On Channel State Inference and Prediction Using Observable Variables in 802.11b Network

Performance of cross-layer protocols that recommend the relay of corrupted packets to higher layers can be improved significantly by accurately inferring/predicting the bit error rate (BER) in the packets. In practice, higher layers observe the bits only after some hard decision. Hence physical layer link-quality indications, such as the signal strength of each individual bit, are not observable at higher layers. Therefore, it is essential to identify practically observable variables, which can be used for reasonably robust channel state inference/prediction (CSI/CSP). Here, inference specifically refers to estimating the BER in an already received packet, while prediction refers to anticipating the BER in a future packet. In this paper, we note that, in practical 802.11b devices, it is possible to acquire a Signal to Silence Ratio (SSR) indication and measure the background traffic intensity (p ) on a per packet basis. This paper, thus presents a measurement-based study that analyzes the utility of SSR and p as side-information for CSI/CSP. In this work, we exploit the method of types to measure the robustness of the observable side-information. Our analysis and simulations based on an extensive set of actual 802.11b traces exhibit the practical utility of the considered observable variables.
Transmission-Distortion Tradeoffs in Network Channel Coding

Network channel coding (NCC) is a framework under which intermediate router/nodes employ encoding/decoding operations to facilitate an efficient multicast delivery of video. The network usage and video distortion is a function of the channel coding rates assigned to nodes in the network. In this paper, we investigate the tradeoff between the total bandwidth usage and a global distortion measure. We propose a dynamic programming based framework to identify the optimal transmission-distortion operating points. The proposed optimal allocation of coding rates is compared with an earlier version of NCC, network embedded FEC (NEF), which optimally places codecs of a fixed channel coding rate within the network. The proposed NCC scheme is shown to achieve significantly improved transmission distortion tradeoffs.
New image transforms using hybrid wavelets and directional filter banks: analysis and design

We propose a new family of perfect reconstruction, non-redundant, and multiresolution geometrical image transforms using the wavelet transform in conjunction with modified versions of directional filter banks (DFB). In the proposed versions of DFB, we use either horizontal or vertical directional decomposition. Taking advantage of the wavelet transform that has efficient nonlinear approximation property, we add the important feature of directionality by applying the modified and regular DFB to the subbands of a few finest wavelet levels. This way we can eliminate a major portion of the artifacts usually introduced when DFB are used. The proposed hybrid wavelets and DFB (HWD) transform family provides visual and PSNR improvements over the wavelet and contourlet transforms.
Design of regular wavelets using a three-step lifting scheme

We propose structural multidimensional multichannel filter banks with desirable numbers of vanishing moments for the analysis and synthesis banks. For a two-channel filter bank, we use a three-step lifting scheme as opposed to the conventional two-step lifting method in order to provide more symmetry between the analysis and synthesis filters. We show that the resulting filters have more regularity, lower frame bounds ratio, and better frequency selectivity. We also extend our design to a general multichannel framework. Three steps of lifting scheme provides us a degree of freedom which we can benefit from toward a more flexible design.