nils-herrmann/clean_scirepeval huggingface dataset:
EPIQ: A new Active Queue Management paradigm for Packet video

In this paper, we introduce a new Active Queue Management (AQM) paradigm and a corresponding congestion control mechanism for a novel video-streaming framework. We call the proposed framework Erasable Packets within Internet Queues (EPIQ). EPIQ exhibits best-effort complexity within the network, similar to the networking complexity of HTTP-based Adaptive Streaming (HAS); yet EPIQ can provide optimal quality-of-experience performance similar to the performance of high-complexity multi-queue solutions. EPIQ exploits the inherent multi-priority nature of video to deliver optimal quality-of-experience without requiring any additional overhead in terms of redundant packets or network resources such as multiple queues and without the need to maintain state information about flows. The novelty of EPIQ is rooted in its packetization. Each EPIQ packet consists of multiple segments, and where each segment carries different priority video content. Under congestion, a network router can simply partially erase only small portions of the EPIQ packets instead of dropping complete packets. We show through an analytical model and extensive simulations that this new paradigm in video streaming provides significant improvements over HAS solutions in terms of quality-of-experience metrics. These significant improvements are achieved while maintaining fairness, stability, and low-complexity within the network.
Fast image super-resolution via selective manifold learning of high-resolution patches

This paper considers the problem of single image super-resolution (SR). Previous example-based SR approaches mainly focus on analyzing the co-occurrence properties of low resolution (LR) and high resolution (HR) patches via dictionary learning. In our recent work , a novel approach (SR via sparse subspace clustering-based linear approximation of manifold or SLAM) has been proposed. In this paper, we further improve the SLAM method by considering and analyzing each tangent subspace as one point in a Grassmann manifold to select an optimal subset of tangent spaces. Furthermore, the optimal subset is clustered hierarchically, which helps in reducing the proposed algorithm's complexity significantly while still preserving the quality of the reconstructed HR image.
Network embedded FEC (NEF) performance over multi-hop wireless channels with memory

Network embedded FEC (NEF) can greatly improve the quality of service for real-time applications in p2p and proxy-based overlay networks [H. Radha and Mingquan Wu, September 2004]. Under NEF, FEC encoders and decoders (codecs) are placed in selected intermediate nodes of an overlay network 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 many multi-hop wireless networks (such as ad hoc networks), end systems also act as routers to forward packets; higher layer functions such as FEC can then be implemented within the intermediate nodes of a multi-hop wireless network. In this paper, we analysis and compare the performance of NEF and end-to-end FEC in a multi-hop wireless channel for packet-loss recovery. We consider Markov erasure channels with memory, and hence, we present a new (and a rather simple and elegant) method for evaluating any desired loss/recovery probability measure for the Gilbert channel model. Consequently, the impact of the loss burst length on the performance of NEF and end-to-end FEC over multi-hop wireless channels are thoroughly analyzed and presented.
Compressive demosaicing for periodic color filter arrays

The utility of Compressed Sensing (CS) for demosaicing of images captured using random panchromatic color filter arrays (CFA) has been investigated in . Meanwhile, most camera manufacturers employ periodic CFAs such as the popular Bayer CFA. In this paper, we derive a CS-based solution to demosaicing images captured using the general class of periodic CFAs. It is well known that periodic CFAs can be designed to effectively separate luminance and chrominance frequency bands [2, 3]. We employ this ability to reduce artifacts associated with luminance-chrominance overlap at the solver side. We show that the modified compressive demo-saicing method coupled with the additional constraint that chrominance channels have smooth surfaces achieves further improved results for most periodic CFAs.
Network Coding with Multi-Generation Mixing: A Generalized Framework for Practical Network Coding

Due to the broadcast nature of wireless networks they have been a natural platform for applying Network Coding (NC). Wireless networks can benefit significantly from NC due to their broadcast nature and the opportunity of enhancing bandwidth utilization. In this paper, we develop Multi-Generation Mixing (MGM), which is a generalized approach for generation based network coding. With traditional generation based NC sender packets are grouped in generations where encoding and decoding are performed on packets that belong to the same generation. In scenarios where losses cause insufficient reception of encoded packets, NC losses occur. NC losses are expensive; the minimum unit of loss is the loss of one generation. The proposed MGM framework allows the encoding among generations for the purpose of enhancing NC decodability. With MGM in scenarios where insufficient number of encodings received of a generation, it is still possible to recover the generation using data encoded in other generations. We develop MGM encoding and decoding approaches, and demonstrate the improvements in performance achieved by MGM. Further, a canonical analytical model for MGM network coding is developed, and, extensive simulations over random wireless networks experiencing random packet losses are presented.
Adaptive Network Coding With Noisy Packets

In this paper we study the problem of employing network coding in presence of bit corruptions in the packets. We consider a network model within which a minimal amount of processing is employed at intermediate nodes. Under such a paradigm the efficiency of a network coding function, is dependent on the bit-error rates (BER) observed in the packets. Hence under time-varying channel conditions the network coding functions need to adapt in accordance to the channel state associated with the packets. We propose a systematic optimization approach to design an adaptive network coding (ANC) scheme which facilitates efficient exchange of mutually independent information. Under the proposed scheme the intermediate nodes relay the packets with or without piggybacking, depending on the corruption in the received packets and in accordance to pre-designed exchange rules. We illustrate the performance benefits of the proposed optimizations on the basis of channel models derived from actual 802.11 b and 802.15.4 measurements.
Image compression using binary space partitioning trees

For low bit-rate compression applications, segmentation-based coding methods provide, in general, high compression ratios when compared with traditional (e.g., transform and subband) coding approaches. In this paper, we present a new segmentation-based image coding method that divides the desired image using binary space partitioning (BSP). The BSP approach partitions the desired image recursively by arbitrarily oriented lines in a hierarchical manner. This recursive partitioning generates a binary tree, which is referred to as the BSP-tree representation of the desired image. The most critical aspect of the BSP-tree method is the criterion used to select the partitioning lines of the BSP tree representation, In previous works, we developed novel methods for selecting the BSP-tree lines, and showed that the BSP approach provides efficient segmentation of images. In this paper, we describe a hierarchical approach for coding the partitioning lines of the BSP-tree representation. We also show that the image signal within the different regions (resulting from the recursive partitioning) can be represented using low-order polynomials. Furthermore, we employ an optimum pruning algorithm to minimize the bit rate of the BSP tree representation (for a given budget constraint) while minimizing distortion. Simulation results and comparisons with other compression methods are also presented.
Maximal Recovery Network Coding Under Topology Constraint

Network coding (NC) within wireless sensor networks (WSNs) can be viewed as the mapping of efficient channel codes to the data generated within the network. In particular, this perspective of code-on-network-graphs (CNG) can be exploited to map source data generated within WSN (of size K) to a variable nodes subset in low-density parity check (LDPC) codes. The resulting fixed size symbol stream when transmitted through the network suffers erasures. At sink, an average of z source symbols can be recovered by employing belief propagation decoding. In this paper, we determine CNG code ensembles that achieve maximal recovery (z/K) for different erasure rates and network topological constraints corresponding to node transmission range. An analytic framework to predict code performance under transmission range constraints is developed. Additionally, necessary condition for code stability was derived using fixed-point stability analysis. Optimal solutions for a WSN with 1000 nodes are determined using differential evolution algorithm. We outline a distributed algorithm for generating a sequence of encoded symbols adhering to the designed code ensemble. The performance of the designed CNG code is demonstrated to be superior to random NC and growth code based ensembles, as well as resilient to network size and inter-connectivity variations.
Multi-view image coding using 3-D voxel models

We propose a multi-view image coding system in 3-D space based on an improved volumetric 3-D reconstruction. Unlike existing multi-view image coding schemes, in which the 3-D scene information is represented by a mesh model as well as the texture data, we use a 3-D voxel model to represent the 3-D scene information of the images to be encoded. Furthermore, we propose important, yet simple, improvements to current 3-D voxel models; these improvements lead to significant coding gain within our 3-D voxel model based compression system. This system provides an elegant framework for employing powerful and mature coding techniques. In particular, we employ the H.264 and 3-D SPIHT coding methods for compressing the proposed 3-D voxel models. Our simulation results clearly illustrate the efficiency and potential of the proposed 3-D voxel model based system.
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.
Practical Distributed Video Coding over visual sensors

In this paper we describe a practical implementation of a distributed video codec deployed on a real visual sensor platform, viz. the MicaZ/Cyclops platform. The codec supports two encoding schemes, one employs Discrete Cosine Transform (DCT) and the other operates on raw pixels. DCT scheme is more costly in terms of computational power consumption, on the other hand it leads to more compression and hence less transmission power consumption. At the same time the DCT scheme may not necessarily achieve minimal overall power consumption (computation and transmission). In this paper we show that the choice of either scheme (DCT or pixel based) depends on the tolerable distortion and power consumption. Results show that for a range of achievable video quality values the DCT scheme demonstrates less overall power consumption. On the other hand for another range of video quality the pixel scheme results in less overall power consumption.
Partial Reed Solomon codes for erasure channels

We introduce a new family of linear block codes, which we refer to as partial Reed Solomon (PRS) codes. These codes are specifically designed and optimized for real-time multimedia communication over packet-based erasure channels. Based on the constraints and flexibilities of real-time applications, we define a performance measure, message throughput (/spl tau//sub m/), which is suitable for these applications. This measure differentiates the notion of optimum codes for the target multimedia applications as compared to performance measures that are used for nonreal-time data. Based on the proposed measure, we combine the advantages of lowering the density of a code for near capacity performance with the high decoding efficiency of Reed Solomon (RS) codes, in order to design optimum PRS codes. Then, we demonstrate, through an example of a binary erasure channel (BEC), that at near-capacity coding rates, the appropriate design of a PRS code can outperform an RS-code. We extend this analysis and optimization for a general BEC over a wide range of channel conditions. Moreover, as compared with RS codes, the proposed PRS codes provide a significantly improved graceful degradation when the number of losses exceeds the number of parity symbols within the code block. This is a highly desirable feature for real-time multimedia applications.
Compressive demosaicing

A typical consumer digital camera uses a Color Filter Array (CFA) to sense only one color component per image pixel. The original three-color image is reconstructed by interpolating the missing color components. This interpolation process (known as demosaicing) corresponds to solving an under-determined system of linear equations. In this paper, we show that by replacing the traditional CFA with a random panchromatic CFA, recent results in the emerging field of Compressed Sensing (CS) can be used to solve the demosaicing problem in a novel way. Specifically, during the image reconstruction process, we exploit the fact that the multi-dimensional color of each pixel has a compressible representation in a (possibly overcomplete) color system. While adhering to the “single color per pixel sensing” constraint at the sensing stage, during the reconstruction process we utilize the inter-pixel correlation by exploiting the compressible representation of the overall image in some sparsifying bases. Depending on the CFA, sparsifying bases and the color system, we form an underdetermined system of linear equations and find the sparsest solution for the color image by utilizing a CS solver. We illustrate that, for natural images, the proposed Compressive Demosaicing (CD) framework visually outperforms leading demosaicing methods in a consistent manner; in many cases it achieves clear visible improvements in a significant way.
Evaluation of Interleaved Source Coding (ISC) over Channel with Memory

Network impairments such as delay and packet losses have severe impact on the presentation quality of many predictive video sources. Interleaved source coding (ISC) is one of the error resilient coding methods for predictive video coded frames transmitted over single erasure channel. ISC has shown to significantly improve the overall quality of predictive coded video stream over a lossy channel. In this paper, ISC is evaluated over channels with memory. In particular, we analyze the impact of packet correlation of the popular Gilbert model on ISC-based packet video over a wide range of packet loss probabilities. Simulations have shown that ISC advances the traditional method as either the loss rate increase or the packet correlation decreases.
Long Range Dependence of IEEE 802.15.4 Wireless Channels

Several works have attempted to determine whether the process that introduces errors into transmissions over a wireless channels is long range dependent (LRD). Most of these efforts were focused on IEEE 802.11b/g channels. Knowledge of LRD is of significant interest to designers of wireless network systems. This experiment-based study attempts to gain a better understanding of the standardized IEEE 802.15.4 low rate-wireless personal area network (LR-WPAN) specification. We determine whether the bit, symbol and packet level error processes are LRD or not. Is the memory length of the channel measurable and finite, or is it sufficiently long to qualify it as an LRD process? We record channel behavior by capturing all transmissions at the receiver including the ones that would under normal operation be dropped and use these to generate residual error traces. We perform statistical data analysis of these traces using various tools for scenarios with and without interference from 802.11b/g sources. For the bit and symbol level error process we conclude that channel memory remains fixed at 2 bits and 2 symbols respectively regardless of channel and environment. For the packet level error process we conclude that the perception of on and off LRD is actually due to interference from co-located 802.11b/g networks.
Scalable MPEG-4 video coding with graceful packet-loss resilience over bandwidth-varying networks

We evaluate the packet loss resilience of the MPEG-4 Fine-Granular-Scalability (FGS) video coding method for Internet streaming applications. Since unrecoverable packet losses are very common over the Internet, the focus of this study is to determine how robust the MPEG-4 FGS coding tool is under unrecoverable IP packet-losses over a wide range of connection qualities (i.e. at various bit-rates). Our extensive study was performed upon an entire set of sequences, bit-rates and packet-loss rates. As shown in this paper, under unequal packet-loss protection, FGS provides a clear advantage over non-scalable coding. If equal packet-loss protection is employed, FGS performance is still considerably better, in particular under moderate-to-high packet-loss ratios.
Low complexity distributed reliability for Wireless Sensor Networks

We consider the distributed wireless sensor network (WSN) reliability problem with the introduction of minimal complexity at sensor nodes. Under the proposed scheme, intermediate sensor nodes, acting as relays to the base station, partially decode the information packets as they traverse the multi-hop wireless network. Nodes collaborate, in a distributed pair-wise manner to allocate the processing budget to individual nodes and obtain near optimal performance. We make recommendations for fast convergence with minimal burden on intermediate nodes. We show that the proposed scheme achieves much better performance than end-to-end channel coding. We use iteratively decodable low density parity check (LDPC) codes to demonstrate the efficiency of our technique.
Wavelet-based contourlet transform and its application to image coding

In this paper, we first propose a new family of geometrical image transforms that decompose images both radially and angularly. Our construction comprises two stages of filter banks that are non-redundant and perfect reconstruction and therefore lead to an overall non-redundant and perfect reconstruction transform. Using the wavelet transform as the first stage, we apply directional filter banks to the wavelet coefficients in such a way to maintain the anisotropy scaling law. Furthermore, we propose a new image coding scheme based on the proposed transform, the wavelet-based contourlet transform (WBCT), using a new contourlet-based set partitioning in hierarchical trees (CSPIHT) algorithm that provides an embedded code. Due to differences in parent-child relationships between the WBCT coefficients and wavelet coefficients, under CSPIHT, we developed an elaborated repositioning algorithm for the WBCT coefficients in such a way that we could scan spatial orientation trees that are similar to the original SPIHT algorithm. Our experiments demonstrate that the proposed approach is efficient in coding images that possess mostly textures and contours. Our simulation results also show that this new coding approach is competitive to the wavelet coder in terms of the PSNR-rate curves, and is visually superior to the wavelet coder for the mentioned images.
Unequal packet loss resilience for fine-granular-scalability video

Several embedded video coding schemes have been recently developed for multimedia streaming over IP. In particular, fine-granular-scalability (FGS) video coding has been recently adopted by the MPEG-4 standard as the core video-compression method for streaming applications. From its inception, the FGS scalability structure was designed to be packet-loss resilient especially under unequal packet-loss protection (UPP). However, since the introduction of FGS, there has not been a comprehensive study evaluating its packet-loss resilience under unrecoverable packet losses that are common in Internet streaming applications. In this paper, we evaluate two important aspects of FGS packet-loss resilience. First, we study the impact of applying UPP between the base- and enhancement-layers on FGS-based streams, and we compare equal packet-loss protection (EPP) with UPP scenarios. Second, we introduce the notion of fine-grained loss protection (FGLP), which is suitable for the FGS enhancement-layer, and we develop an analytical framework for evaluating FGLP bounds. Based on these bounds, we show the impact of applying fine-grained protection to the FGS enhancement-layer for different types of video sequences and over a wide range of bit-rates and packet-loss ratios. As illustrated by our extensive simulation results, applying 1) UPP between the base- and enhancement-layers and 2) FGLP for the FGS enhancement-layer can provide significant resilience under moderate-to-high packet-loss ratios (e.g., 5-10%). Furthermore, the merits of this new packet-loss protection technique go beyond the FGS coding scheme, because FGLP can be successfully applied to improve the resilience to packet-losses of other embedded video coding techniques.
Cross-layer protocol design for real-time multimedia applications over 802.11 b networks

Inherent vulnerability of the wireless medium renders it more susceptible to errors and losses than classical wired media. In this paper, we evaluate the suitability of protocols and strategies across different layers of the stack to provide real-time services over 802.11b wireless LANs. More specifically, within the context of cross-layer design, we compare the performance of UDP with UDP lite - a proposed framework, which improves bandwidth utilization by delivering partially damaged packets to the realtime application. First, we study the high-level end-to-end throughput improvement achieved by making cross-layer modifications to support a UDP lite framework. We compare the quality of perceived media rendered by UDP (dropped packets only) and UDP lite (dropped and corrupted packets). This formulates one of the key findings of this study, that is, although UDP lite improves the overall high-level throughput by relaying corrupted packets to the real-time application, it fails to provide significant enhancement in perceived media quality. This can, in part, be attributed to the bursty nature of errors and losses that we observed at the application layer regardless of the selected transport protocol. Finally, we compare the error-recovery/concealment overhead required by UDP and UDP lite in order to deliver lossless multimedia. We conclude that the overhead required by UDP lite is considerably lower than UDP, since the received corrupted packets that are delivered by UDP lite (but not by UDP) facilitate error-recovery.