nils-herrmann/clean_scirepeval huggingface dataset:
A new class of B/W halftoning algorithms

A new class of dithering algorithms for black and white (B/W) images is presented. The basic idea behind the technique is to divide the image into small blocks and minimize the distortion between the original continuous-tone image and its low-pass-filtered halftone. This corresponds to a quadratic programming problem with linear constraints, which is solved via standard optimization techniques. Examples of B/W halftone images obtained by this technique are compared to halftones obtained via existing dithering algorithms.
Local Estimation of Probabilities of Direct and Staggered Collisions in 802.11 WLANs

Current 802.11 networks do not typically achieve the maximum potential throughput despite link adaptation and cross-layer optimization techniques designed to alleviate many causes of packet loss. A primary contributing factor is the difficulty in distinguishing between various causes of packet loss, including collisions caused by high network use, co-channel interference from neighboring networks, and errors due to poor channel conditions. In this paper, we propose a novel method for estimating various collision type probabilities locally at a given node of an 802.11 network. Our approach is based on combining locally observable quantities with information observed and broadcast by the access point (AP) in order to obtain partial spatial information about the network traffic. We provide a systematic assessment and definition of the different types of collision, and show how to approximate each of them using only local and AP information. Additionally, we show how to approximate the sensitivity of these probabilities to key related configuration parameters including carrier sense threshold and packet length. We verify our methods through NS-2 simulations, and characterize estimation accuracy of each of the considered collision types.
Theoretical and numerical aspects of an SVD-based method for band-limiting finite-extent sequences

The authors present an SVD-based method for band-limiting over-sampled discrete-time finite-extent sequences. For this purpose, they show that finite-extent band limitation is best defined in terms of the discrete prolate spheroidal sequences rather than complex exponentials. Their method has maximum energy concentration as defined in the paper, its dimension agrees asymptotically with Slepian's (1978) dimension result, and the method specializes correctly to the discrete-time Fourier transform as the sample size tends to infinity. They propose an efficient computational method, based on the Lanczos algorithm, for computing only the necessary singular vectors. The SVD is signal-independent, only needs to be done once and can be precomputed. The SVD-based band limitation itself is not necessarily much slower than the fast Fourier transform for sample sizes on the order of 4096.
Applications Of Multi-resolution Analysis To Images

The concept of a multiresolution decomposition is a powerful tool in single and multidimensional signal processing applications. The basic idea behind them is to examine signals “under a microscope” with different zoom factors. In their current form proposed in the literature, the shape of the underlying analyzing filter/lens remains the same from .one resolution to the next. While this results in a compact representation of signals that “look” the same across different scales, such as fractal type signals, it is not necessarily an optimal way of analyzing signal which have widely different characteristics at different scales and therefore need to be processed with different shape filters at different resolutions. In this presentation, we investigate applications of multi-resolution analysis to images from two complementary viewpoints. On the one hand we are interested in wavelet decompositions, in which the image is analyzed by functions which are formed by dilating and shifting a basic wavelet function. Such a representation can be advantageous in the recognition and exploitation of self-similarity in fractal type images. On the other hand, taking a more general view of multi-resolution decomposition, we abandon the traditional requirement that each successive resolution subspace, , be generated by a basis of dilated basis vectors of K+1. Such an approach, being less constrained than the traditional concept of a multi-resolution decomposition, is better suited to some adaptive coding approaches. The main characteristic of fractal objects is that a small number of parameters should be sufficient for their representation, and the main goal of a coding or image representation scheme would be that of finding these parameters. It has been shown that the redundancy of a fractal signal with respect to scale variation is mirrored by its wavelet decomposition, in that the wavelet transform at a finer scale is obtainable from the wavelet transform at coarser scales according to a causal prescription. In this presentation
Selection of temporally dithered codes for increasing virtual depth of field in structured light systems

Temporally dithered codes have recently been used for depth reconstruction of fast dynamic scenes using off-the-shelf DLP Projectors. Even though temporally dithered codes overcome the DLP projector's limited frame rate, limitations with the optics create challenges for using these codes in an actual structured light system. Specifically, to maximize the amount of light leaving the projector, projector lenses are designed to have large apertures resulting in projected patterns that appear in focus over a narrow depth of field. In this paper, we propose a method to design temporally dithered codes in order to extend the virtual depth of field of a structured light system. By simulating the PWM sequences of a DLP projector and the blurring process from the projector lens, we develop algorithms for designing and decoding projection patterns in the presence of out of focus blur. Our simulation results show a 47% improvement in the depth of field when compared against randomly selected codewords.
Single view pose estimation of mobile devices in urban environments

Pose estimation of mobile devices is useful for a wide variety of applications, including augmented reality and geo-tagging. Even though most of today's cell phones are equipped with sensors such as GPS, accelerometers, and gyros, the pose estimated via these is often inaccurate, particularly in urban environments. In this paper, we describe an image based localization algorithm for estimating the pose of cell phones in urban environments. Our proposed approach solves for a homography transformation matrix between the cell phone image and a matching database image, constrained by knowledge of the change in orientation obtained from the cell phone gyro, and augmented with 3D information from the database to achieve an estimate of pose which improves upon readings from the GPS and compass. We characterize the performance of this approach for a dataset in Oakland, CA and show that for a query set of 92 images, our computed location (yaw) is within 10 meters (degrees) for 92% (96%) of queries as compared to 31% (26%) for the GPS (compass) on the cell phone.
Multirate 3-D subband coding of video

We propose a full color video compression strategy, based on 3-D subband coding with camera pan compensation, to generate a single embedded bit stream supporting multiple decoder display formats and a wide, finely gradated range of bit rates. An experimental implementation of our algorithm produces a single bit stream, from which suitable subsets are extracted to be compatible with many decoder frame sizes and frame rates and to satisfy transmission bandwidth constraints ranging from several tens of kilobits per second to several megabits per second. Reconstructed video quality from any of these bit stream subsets is often found to exceed that obtained from an MPEG-1 implementation, operated with equivalent bit rate constraints, in both perceptual quality and mean squared error. In addition, when restricted to 2-D, the algorithm produces some of the best results available in still image compression.
In-loop atom modulus quantization for matching pursuit and its application to video coding

This paper provides a precise analytical study of the selection and modulus quantization of matching pursuit (MP) coefficients. We demonstrate that an optimal rate-distortion trade-off is achieved by selecting the atoms up to a quality-dependent threshold, and by defining the modulus quantizer in terms of that threshold. In doing so, we take into account quantization error re-injection resulting from inserting the modulus quantizer inside the MP atom computation loop. In-loop quantization not only improves coding performance, but also affects the optimal quantizer design for both uniform and nonuniform quantization. We measure the impact of our work in the context of video coding. For both uniform and nonuniform quantization, the precise understanding of the relation between atom selection and quantization results in significant improvements in terms of coding efficiency. At high bitrates, the proposed nonuniform quantization scheme results in 0.5 to 2 dB improvement over the previous method.
Computer-aided phase shift mask design with reduced complexity

We propose a new approach to systematic phase shift mask design. In doing so, we constrain the complexity of the mask at a pre-specified level by limiting the number of "features" on the mask. We then optimize the location, size and phase of the features so as to achieve a desired intensity pattern on the wafer. The main advantage of this object-based approach over our previous pixel-based solution is that it results in substantially larger assisting phase shift features, and is therefore easier to fabricate. Our approach can also be used to design masks with proper bias and/or extension of the depth of focus. We show examples of contact hole, bright line and chromeless line-space mask designs. Finally, we show experimental results using the new approach.
Lossless compression of VLSI layout image data

We present a novel lossless compression algorithm called Context Copy Combinatorial Code (C4), which integrates the advantages of two very disparate compression techniques: context-based modeling and Lempel-Ziv (LZ) style copying. While the algorithm can be applied to many lossless compression applications, such as document image compression, our primary target application has been lossless compression of integrated circuit layout image data. These images contain a heterogeneous mix of data: dense repetitive data better suited to LZ-style coding, and less dense structured data, better suited to context-based encoding. As part of C4, we have developed a novel binary entropy coding technique called combinatorial coding which is simultaneously as efficient as arithmetic coding, and as fast as Huffman coding. Compression results show C4 outperforms JBIG, ZIP, BZIP2, and two-dimensional LZ, and achieves lossless compression ratios greater than 22 for binary layout image data, and greater than 14 for gray-pixel image data
Efficient video similarity measurement with video signature

The proliferation of video content on the Web makes similarity detection an indispensable tool in Web data management, searching, and navigation. We propose a number of algorithms to efficiently measure video similarity. We define video as a set of frames, which are represented as high dimensional vectors in a feature space. Our goal is to measure ideal video similarity (IVS), defined as the percentage of clusters of similar frames shared between two video sequences. Since IVS is too complex to be deployed in large database applications, we approximate it with Voronoi video similarity (VVS), defined as the volume of the intersection between Voronoi cells of similar clusters. We propose a class of randomized algorithms to estimate VVS by first summarizing each video with a small set of its sampled frames, called the video signature (ViSig), and then calculating the distances between corresponding frames from the two ViSigs. By generating samples with a probability distribution that describes the video statistics, and ranking them based upon their likelihood of making an error in the estimation, we show analytically that ViSig can provide an unbiased estimate of IVS. Experimental results on a large dataset of Web video and a set of MPEG-7 test sequences with artificially generated similar versions are provided to demonstrate the retrieval performance of our proposed techniques.
Adaptive Carrier-Sensing for Throughput Improvement in IEEE 802.11 Networks

As a Carrier Sense Multiple Access (CSMA) network, the performance of IEEE 802.11 networks highly depends on the accuracy of the carrier sensing procedure. However, conventional carrier sensing approaches suffer from the well known hidden and exposed node problems, adversely affecting aggregate throughput of the IEEE 802.11 networks. In this paper, we propose a novel scheme through which each station can adaptively select its Carrier Sense Threshold (CST) in order to mitigate the hidden/exposed node problems. The basic idea behind our approach is for the Access Point (AP) to periodically transmit a Busy/Idle (BI) signal to all the stations. Individual stations then use the BI signal from the AP together with their own local BI signal in order to adjust their CST. We use NS-2 simulations to show that our approach can enhance the aggregate throughput by as much as 50%.
Tone behavior of the double loop /spl Sigma//spl Delta/ modulator with unstable filter dynamics

In this paper we analyze the tone behavior of the double loop /spl Sigma//spl Delta/ modulator with unstable filter dynamics. We show that some unstable limit cycles have an attractor region in their neighborhood which results in tones in the spectrum corresponding to the fundamental or harmonics of these limit cycles. We develop the tools to determine whether or not an unstable limit cycle has an attractor in its neighborhood and obtain a method to approximate the boundaries of these attractors. We then propose a novel /spl Sigma//spl Delta/ structure for tone removal.<<ETX>>
Transmission protocols for streaming video over wireless

Rate control is an important issue in video streaming applications for both wired and wireless networks. A widely accepted rate control method in wired networks is TCP friendly equation based rate control (S. Floyd et al., Aug 2000), i.e. TFRC. However, it assumes that packet loss in wired networks is primarily due to congestion, and as such is not applicable to wireless networks in which the bulk of packet loss is due to error at the physical layer. In this work, we show multiple TFRC connection is an efficient end-to-end rate control solution for wireless video streaming applications. The approach not only avoids modifications to the network infrastructure or network protocol, but also results in full utilization of the wireless channel. Video streaming related simulations arc carried out to show the efficiency of our proposed approach.
Matching pursuit video coding at very low bit rates

Matching pursuits refers to a greedy algorithm which matches structures in a signal to a large dictionary of functions. In this paper, we present a matching-pursuit based video coding system which codes motion residual images using a large dictionary of Gabor functions. One feature of our system is that bits are assigned progressively to the highest-energy areas in the motion residual image. The large dictionary size is another advantage, since it allows structures in the motion residual to be represented using few significant coefficients. Experimental results compare the performance of the matching-pursuit system to a hybrid-DCT system at various bit rates between 6 and 128 kbit/s. Additional experiments show how the matching pursuit system performs if the Gabor dictionary is replaced by an 8/spl times/8 DCT dictionary.
Optimal decoding for data acquisition applications of sigma delta modulators

A class of optimal nonlinear decoding algorithms is proposed for data acquisition applications of sigma-delta ( Sigma Delta ) modulators. The technique is applicable to all current Sigma Delta structures, including single and double-loop, cascade, and interpolative modulators. While the performance of the technique is identical to that of other optimal nonlinear decoding schemes such as table lookup, it is considerably simpler to implement. Numerical results are presented to compare its performance to that of linear decoders. Effects of circuit imperfections on performance are examined.
Matching pursuits multiple description coding for wireless video

Multiple description coding (MDC) is an error-resilient source coding scheme that creates multiple bitstreams of approximately equal importance. The reconstructed signal based on any single bitstream has an acceptable quality. However, a higher quality reconstruction can be achieved with a larger number of bitstreams. We develop a multiple description video coding scheme based on a three-loop structure. We modify the discrete cosine transform structure to the matching pursuits framework and evaluate performance gain using maximum-likelihood (ML) enhancement when both descriptions are available. We find that ML enhancement works best for low motion sequences and results in gains of up to 1.3 dB in terms of average PSNR. Rate distortion performance is characterized. Performance comparison is made between our MDC scheme and single description coding (SDC) schemes over lossy channels, including two state Markov channels and Rayleigh fading channels. We find that MDC outperforms SDC in bursty slowly varying environments. In the case of Rayleigh fading channels, interleaving helps SDC close the gap and even outperform MDC depending on the amount of interleaving performed, at the expense of additional delay.
A common framework for rate and distortion based scaling of highly scalable compressed video

Scalability refers to the ability to modify the resolution and/or bit rate associated with an already compressed data source in order to satisfy requirements which could not be foreseen at the time of compression. A number of researchers have already demonstrated the feasibility of efficient scalable image and video compression. The principle focus of this paper is to describe data structures for highly scalable compressed video, which are able to support simple, generic scaling approaches for both constant bit rate and constant distortion scaling criteria. Interactive video material presents particular challenges when the data stream is to be scaled to maintain an approximately constant level of distortion, rather than just a constant bit rate. Special attention is paid, therefore, to the development of generic, robust scaling algorithms for such applications. The data structures and scaling methodologies developed are particularly appealing for the distribution of highly scalable compressed video over heterogeneous media, because they simultaneously support both variable bit rate (VBR) and constant bit rate (CBR) services with a wide range of available service qualities, using only simple, generic mechanisms for scaling. The performance of the proposed scaling methodologies is experimentally investigated using a highly scalable video compression algorithm, which is able to achieve comparable compression performance to that of the inherently nonscalable MPEG-1 compression standard.
Efficient video similarity measurement with video signature

The video signature method has previously been proposed as a technique to summarize video efficiently for visual similarity measurements (see Cheung, S.-C. and Zakhor, A., Proc. SPIE, vol.3964, p.34-6, 2000; ICIP2000, vol.1, p.85-9, 2000; ICIP2001, vol.1, p.649-52, 2001). We now develop the necessary theoretical framework to analyze this method. We define our target video similarity measure based on the fraction of similar clusters shared between two video sequences. This measure is too computationally complex to be deployed in database applications. By considering this measure geometrically on the image feature space, we find that it can be approximated by the volume of the intersection between Voronoi cells of similar clusters. In the video signature method, sampling is used to estimate this volume. By choosing an appropriate distribution to generate samples, and ranking the samples based upon their distances to the boundary between Voronoi cells, we demonstrate that our target measure can be well approximated by the video signature method. Experimental results on a large dataset of Web video and a set of MPEG-7 test sequences with artificially generated similar versions are used to demonstrate the retrieval performance of our proposed techniques.
Efficient video similarity measurement and search

We consider the use of meta-data and/or video-domain methods to detect similar videos on the Web. Meta-data is extracted from the textual and hyperlink information associated with each video clip. In the video domain, we apply an efficient similarity detection algorithm called video signature. The idea is to form a signature for each clip by selecting a small number of its frames that are most similar to a set of random seed images. We then apply a statistical pruning algorithm to allow fast detection on very large databases. Using a small ground-truth set, we achieve 90% recall and 95% precision using only 8% of the total number of operations required without pruning. For a database of around 46,000 video clips crawled from the Web, the video signature technique significantly outperforms meta-data in precision and recall. We show that even better performance can be achieved by combining them together. Based on our measurements, each video clip in our database has, on average, 1.53 similar copies.
Multiple Tree Video Multicast Over Wireless Ad Hoc Networks

In this paper, we propose multiple tree construction schemes and routing protocols for video streaming over wireless ad hoc networks. The basic idea is to split the video into multiple parts and send each part over a different tree, which are constructed to be disjoint with each other so as to increase robustness to loss and other transmission degradations. Specifically, we propose two novel multiple tree multicast protocols. Our first scheme constructs two disjoint multicast trees in a serial, but distributed fashion, and is referred to as serial multiple disjoint tree multicast routing protocol. It achieves reasonable tree connectivity while maintaining disjointness of two trees. In order to reduce routing overhead and construction delay, we further propose parallel multiple nearly-disjoint multicast trees protocol, which is also shown to achieve reasonable tree connectivity. Simulations show that resulting video quality for either scheme is significantly higher than that of single tree multicast, with similar routing overhead and forwarding efficiency
A Method for Estimating Access Delay Distribution in IEEE 802.11 Networks

The volume of multimedia traffic over wireless networks has been steadily increasing over the past decade. Unlike web browsing applications, multimedia data needs to satisfy stringent delay requirements since late packets are as good as lost packets. In this paper, we present a framework for the nodes in 802.11 networks to estimate the distribution of uplink access delay in Distributed Coordination Function (DCF) MAC mechanism using locally available information. The access delay for a packet is defined as the time between the packet arriving at the head of line of MAC queue, and its ACK being received. In our proposed framework, each node periodically records channel occupancy information to estimate the distribution of access delay. We use NS-2 simulations to verify the accuracy of our proposed approach.