nils-herrmann/clean_scirepeval huggingface dataset:
Fast similarity search and clustering of video sequences on the world-wide-web

We define similar video content as video sequences with almost identical content but possibly compressed at different qualities, reformatted to different sizes and frame-rates, undergone minor editing in either spatial or temporal domain, or summarized into keyframe sequences. Building a search engine to identify such similar content in the World-Wide Web requires: 1) robust video similarity measurements; 2) fast similarity search techniques on large databases; and 3) intuitive organization of search results. In a previous paper, we proposed a randomized technique called the video signature (ViSig) method for video similarity measurement. In this paper, we focus on the remaining two issues by proposing a feature extraction scheme for fast similarity search, and a clustering algorithm for identification of similar clusters. Similar to many other content-based methods, the ViSig method uses high-dimensional feature vectors to represent video. To warrant a fast response time for similarity searches on high dimensional vectors, we propose a novel nonlinear feature extraction scheme on arbitrary metric spaces that combines the triangle inequality with the classical Principal Component Analysis (PCA). We show experimentally that the proposed technique outperforms PCA, Fastmap, Triangle-Inequality Pruning, and Haar wavelet on signature data. To further improve retrieval performance, and provide better organization of similarity search results, we introduce a new graph-theoretical clustering algorithm on large databases of signatures. This algorithm treats all signatures as an abstract threshold graph, where the distance threshold is determined based on local data statistics. Similar clusters are then identified as highly connected regions in the graph. By measuring the retrieval performance against a ground-truth set, we show that our proposed algorithm outperforms simple thresholding, single-link and complete-link hierarchical clustering techniques.
A note on the sampling of zero crossings of two-dimensional signals

Curtis et al. applied a theorem due to Bezout to show that almost all continuous, periodic, band-limited two-dimensional signals can be reconstructed from at most 4(N<inf>1</inf>+ N<inf>2</inf>)<sup>2</sup>zero-crossing samples where N<inf>1</inf>and N<inf>2</inf>is the number of Fourier coefficients in the signal. In this letter we prove a new version of Bezout's theorem and apply it to the above problem to provide a more lenient sampling requirement of at most 8N<inf>1</inf>N<inf>2</inf>zero-crossing samples.
Lossless Compression Algorithms for Post-OPC IC Layout

An important step in today's integrated circuit (IC) manufacturing is optical proximity correction (OPC). While OPC increases the fidelity of pattern transfer to the wafer, it also results in significant increase in IC layout file size. In this paper, we develop two techniques for compressing post-OPC layout data while remaining compliant with existing industry standard data formats such as OASIS and GDSII. The motivation for doing so is for the resulting compressed files to be viewed and edited by any industry standard CAD tools without a decoder. Our approach is to eliminate redundancies in the representation of the geometric data by finding repeating groups of polygons between multiple cells as well as within a cell. We refer to the former as "inter-cell sub-cell detection" and the later as "intra-cell sub-cell detection". Both problems are NP hard, and as such, we propose two sets of greedy algorithms to solve them. We show the results of our proposed inter-cell and intra-cell algorithms on actual 90nm, 130nm, and 180nm IC layouts.
Bit allocation for joint source/channel coding of scalable video

We propose an efficient bit allocation algorithm for a joint source/channel video codec over noisy channels. The approach is to distribute the available source and channel coding bits among the subbands in such a way that the expected distortion is minimized. The constructed distortion curves bound the performance degradation should the channel be estimated incorrectly. The algorithm can be used in other similar distortion minimization problems with two constraints, such as power or complexity.
Optical Proximity Correction With Linear Regression

An important step in today's integrated circuit (IC) manufacturing is optical proximity correction (OPC). In model-based OPC, masks are systematically modified to compensate for the nonideal optical and process effects of an optical lithography system. The polygons in the layout are fragmented, and simulations are performed to determine the image intensity pattern on the wafer. If the simulated pattern on the wafer does not match the desired one, the mask is perturbed by moving the fragments. This iterative process continues until the pattern on the wafer matches the desired one. Although OPC increases the fidelity of pattern transfer to the wafer, it is quite CPU intensive due to the simulations performed at each iteration. In this paper, linear regression techniques from statistical learning are used to predict the fragment movements. The goal is to reduce the number of iterations required in model-based OPC by using a fast, computationally efficient linear regression solution as the initial guess to model-based OPC. Experimental results show that fragment movement predictions via linear regression model significantly decrease the number of iterations required in model-based OPC, thereby decreasing the product development time in IC design and manufacturing.
3D model generation for cities using aerial photographs and ground level laser scans

In this paper we describe techniques for 3D textured model construction of urban areas using acquisition devices such as intensity cameras, as well as 2D laser scanner. Our experimental set up consists of a truck equipped with one camera and two fast, inexpensive 2D laser scanner, traveling on city streets under normal traffic conditions. The horizontal laser scans are used to determine the approximate component of motion along the movement of the acquisition vehicle. The vertical scanner is used to build 3D models of the facade of the buildings. To improve the accuracy of localization of the truck and hence our resulting 3D models of the city, two different methods are developed and compared: the first method employs a correlation technique and the second method is based on Markov Monte Carlo localization. Both techniques use digital road maps and aerial photographs in conjunction with laser scans. A fairly accurate textured, 3D model of downtown area has been acquired in a matter of few minutes, limited only by traffic conditions during the data acquisition phase.
Halftone to continuous-tone conversion of error-diffusion coded images

Considers the problem of reconstructing a continuous-tone (contone) image from its halftoned version, where the halftoning process is done by error diffusion. The authors present an iterative nonlinear decoding algorithm for halftone-to-contone conversion and show simulation results that compare the performance of the algorithm to that of conventional linear low-pass filtering. They find that the new technique results in subjectively superior reconstruction. As there is a natural relationship between error diffusion and SigmaDelta modulation, the reconstruction algorithm can also be applied to the decoding problem for SigmaDelta modulators.
Path diversity and bandwidth allocation for multimedia streaming

The recent advent of widely available broadband Internet access has resulted in an explosive growth of new video streaming applications and research into methods to efficiently support, such applications over today's Internet. Many approaches, including source and channel coding techniques, have been proposed to deal with the delay, loss, and time-varying characteristics of best-effort packet-switched networks. In this paper we present two class of techniques for such networks: the first set of techniques is designed for streaming to receivers with bandwidth-limited last mile connections to the Internet, while the second set explores techniques relying on path diversity to handle situations in which the path to the video source, and not the access link itself, causes degradation in the quality of the video stream. We show results of simulations and Internet experiments, demonstrating the effectiveness of the two techniques.
An optimization approach for removing blocking effects in transform coding

One drawback of the discrete cosine transform (DCT) is visible block boundaries due to coarse quantization of the coefficients. Most restoration techniques for the removing blocking effect are variations of low-pass filtering, and as such, result in unnecessary blurring. The authors propose a new approach for reducing the blocking effect which can be applied to conventional transform coding without introducing additional information or significant blurring. The method exploits the correlation between the intensity values of boundary pixels of two neighboring blocks. It is based on the theoretical and empirical observation that under mild assumptions, quantization of the DCT coefficients of two neighboring blocks increases the expected value of the mean squared difference of slope (MSDS) between the slope across two adjacent blocks, and the average between the boundary slopes of each of the two blocks. The amount of this increase is dependent upon the width of quantization intervals of the transform coefficients. Therefore, among all permissible inverse quantized coefficients, the set which reduces the expected value of this MSDS by an appropriate amount is most likely to decrease the blocking effect. To estimate the set of unquantized coefficients, the authors solve a constrained quadratic programming problem. The approach is based on the gradient projection method. It is shown that from a subjective viewpoint, the blocking effect is less noticeable in the author' processed images than in the ones using existing filtering techniques.
Fast approximation for geometric classification of LiDAR returns

Current LiDAR classification methods are excessively slow to be used in real-time navigation systems, even though they are useful for human perception. These methods typically analyze curvature by applying Principal Component Analysis (PCA) to each point in a point cloud. For variable-density aerial LiDAR obtained by at a shallow angle with respect to the ground rather than in a top-down fashion, the variations in density pose special challenges in terms of choosing the appropriate PCA parameters. In this paper we use gridded approximate nearest neighbor searches for fast classification of geometric features in large LiDAR point clouds. The underlying algorithm exploits spatial hashes and the forgiving nature of PCA as a part of geometric classification. We show a factor of 10–20 speed up for both actual and simulated point clouds with little or no loss in classification performance. Our approach is applicable to both uniform and variable-density aerial LiDAR datasets.
Receiver-driven bandwidth sharing for TCP

Applications using TCP, such as Web-browsers, ftp, and various P2P programs, dominate most of the Internet traffic today. In many cases the last-hop access links are bottlenecks due to their limited bandwidth capability with users running many simultaneous network applications. Standard TCP shares bottleneck link capacity according to connection round-trip time (RTT), and may result in a bandwidth partition which does not necessarily coincide with the user's desires. We present a receiver-based control system for allocating bandwidth among TCP flows according to user preferences. Our system does not require any changes to network infrastructure, and works with standard TCP senders. NS-2 simulations, as well as actual Internet experiments, show that our system achieves desired bandwidth allocation in a wide variety of scenarios including interfering cross-traffic. We also demonstrate the viability of our system in multimedia streaming applications over TCP.
Resilient compression of video for transmission over the Internet

We introduce a point to point video transmission scheme over the Internet combining a low-delay TCP-friendly transport protocol in conjunction with a novel compression method that is error resilient and bandwidth scalable. Compressed video is packetized into individually decodable packets that are of equal expected visual importance. As a result, relatively constant video quality can be achieved at the receiver under lossy conditions. The packets can be truncated to meet the time varying bandwidth imposed by the transport protocol. Actual Internet experiments together with simulations are used to evaluate the performance of the overall scheme.
Projector domain phase unwrapping in a structured light system with stereo cameras

Phase-shifted sinusoids are commonly used as projection patterns in structured light systems consisting of projectors and cameras. They require few image captures per 3D reconstruction and have low decoding complexity. Recently, structured light systems with a projector and a pair of stereo cameras have been used in order to overcome the traditional phase discontinuity problem and allow for the reconstruction of scenes with multiple objects. In this paper, we propose a new approach to the phase unwrapping process in such systems. Rather than iterating through all pixels in the two cameras to determine the global phase of each pixel, we iterate through the projector pixels to solve for correspondences between the two camera views. An energy minimization framework is applied to these initial estimated correspondences to enforce smoothness and to fill in missing pixels. Unlike existing approaches, our method allows simultaneous unwrapping of both camera images and enforces consistency across them. We demonstrate the effectiveness of our approach experimentally on a few scenes.
Arbitrary view generation for three-dimensional scenes from uncalibrated video cameras

This paper focuses on the representation and arbitrary view generation of three dimensional (3-D) scenes. In contrast to existing methods that construct a full 3-D model or those that exploit geometric invariants, our representation consists of dense depth maps at several preselected viewpoints from an image sequence. Furthermore, instead of using multiple calibrated stationary cameras or range data, we derive our depth maps from image sequences captured by an uncalibrated camera. We propose an adaptive matching algorithm which assigns various confidence levels to different regions. Nonuniform bicubic spline interpolation is then used to fill in low confidence regions in the depth maps. Once the depth maps are computed at preselected viewpoints, the intensity and depth at these locations are used to reconstruct arbitrary views of the 3-D scene. Experimental results are presented to verify our approach.
Disk-based storage for scalable video

We consider the placement of scalable video data on single and multiple disks for storage and real-time retrieval. For the single-disk case, we extend the principle of constant frame grouping from constant bit rate (CBR) to variable bit rate (VBR) scalable video data. When the number of admitted users exceeds the server capacity, the rate of data sent to each user is reduced to relieve the disk system overload, offering a graceful degradation in comparison with nonscalable data. We examine the qualities of video reconstructions obtained from a real disk video server and find the scalable video more visually appealing. In the VBR case, scalability is also used to improve interactivity by reducing the delay associated with using interactive functions in a predictive admission control environment. Finally, we consider the multiple disk scenario and prove that periodic interleaving results in lower system delay than striping in a video server using round-robin scheduling. We verify the results through detailed simulation of a four-disk array.
Matching pursuits based multiple description video coding for lossy environments

Multiple description coding (MDC) is an error resilient source coding scheme that creates multiple descriptions of the source with the aim of providing an acceptable reconstruction quality when only one description is received, and improved quality as more descriptions become available. Recently, we developed a matching pursuit multiple description video coder (MP-MDVC) based on a three loop structure originally proposed by Reibman and her colleagues (MTDC). While the MP-MDVC outperforms the discrete cosine transform based MTDC. it is not optimized for lossy environments. In this paper, we extend MP-MDVC by considering the network loss characteristics when coding multiple descriptions. In particular, we propose a fast steepest descent algorithm for creating multiple descriptions that results in minimum expected distortion, given network outage probability, bandwidth constraints, and maximum allowable distortion for each description. Analytical and experimental results show that by taking network loss characteristics into account, our approach outperforms existing MP-MDVC techniques.
Real-Time Internet Video Using Error Resilient Scalable Compression and TCP-Friendly Transport Protocol

We introduce a point to point real-time video transmission scheme over the Internet combining a low-delay TCP-friendly transport protocol in conjunction with a novel compression method that is error resilient and bandwidth-scalable. Compressed video is packetized into individually decodable packets of equal expected visual importance. Consequently, relatively constant video quality can be achieved at the receiver under lossy conditions. Furthermore, the packets can be truncated to instantaneously meet the time varying bandwidth imposed by a TCP-friendly transport protocol. As a result, adaptive flows that are friendly to other Internet traffic are produced. Actual Internet experiments together with simulations are used to evaluate the performance of the compression, transport, and the combined schemes.
Connectivity for multiple multicast trees in ad hoc networks

In this paper, we first define level of tree connectivity for multicast routing schemes in wireless ad hoc networks. Then given the level of tree connectivity, we examine the relation between node density required for single and double tree schemes both analytically and experimentally. For high density wireless ad hoc networks and a given level of tree connectivity, our analysis shows that the difference between required node densities for the two schemes is no larger than a logarithm factor of double tree scheme.
Block based halftoning at 45 degree "screen" angle

Zakhor et al. (1993) introduced a block based halftoning algorithm which was based on dividing the image into small blocks and minimizing the distortion between the original continuous tone image and the filtered version of its halftone. While the resulting images using this technique look promising, there remains an underlying "cheesecloth screen" artifact. The present authors show that by rotating the small optimization blocks by 45 degrees, i.e. turning them into rhombic blocks, one can eliminate this horizontal/vertical artifact, thus improving the perceived quality of the resulting halftoned images. They also propose ways of speeding up the optimization algorithm by one or two orders of magnitude; of course, this is achieved at the expense of slightly lower quality halftoned images.<<ETX>>
Adaptive packetization for error-prone transmission over 802.11 WLANs with hidden terminals

Collision and fading are the two main sources of packet loss in wireless local area networks (WLANs) and as such, both are affected by the packetization at the medium access control (MAC) layer.While a larger packet is preferred to balance protocol header overhead, a shorter packet is less vulnerable to packet loss due to channel fading errors or staggered collisions in the presence of hidden terminals. Direct collisions due to backoff are not affected by packet size. Recently, Krishnan et. al. have developed a new technique for estimating probabilities of various components of packet loss, namely, direct and staggered collisions and fading. Motivated by this work, in this paper, we exploit ways in which packetization can be used to improve throughput performance of WLANs. We first show analytically that the effective throughput is a unimodal function of the packet size when considering both channel fading and staggered collisions. We then develop a measurement-based algorithm based on golden section search to arrive at an optimal packet size for MAC-layer transmissions. Our simulations demonstrate that packetization based on our search algorithm can greatly improve the effective throughput of sensing-limited nodes, and reduce video frame transfer delay in WLANs.
Multiframe spatial resolution enhancement of color video

This paper focuses on the spatial resolution enhancement of a video sequence. In contrast to previous work with grayscale images and highly constrained motion, we present a technique for color video frames with general motion. The method consists of determining subpixel motion estimation between video frames and subsequently using these estimates along with the original low resolution frames to iteratively create a sequence of enhanced resolution frames. We present a novel motion estimation technique based on determining a set of candidate motion estimates per pixel. Experimental results for video sequences containing general motion are presented to verify our technique. Enhanced frames using this technique show significant improvement in both SNR and perceived visual quality over methods such as bilinear and cubic B-spline interpolation.
Performance analysis of an H.263 video encoder for VIRAM

VIRAM (vector intelligent random access memory) is a vector architecture processor with embedded memory, designed for portable multimedia processing devices. Its vector processing capability results in high performance multimedia processing, while embedded DRAM technology provides high memory bandwidth with low energy consumption. We evaluate and compare the performance of VIRAM to digital signal processors (DSPs) and conventional SIMD (single instruction multiple data) media extensions in the context of video coding. In particular, we examine motion estimation (ME) and the discrete cosine transform (DCT) which have been shown to dominate typical video encoders such as H.263. We show that VIRAM outperforms other architectures by 4.6/spl times/ to 8.7/spl times/ in computing ME and by 1.2/spl times/ to 5.0/spl times/ in computing DCT.
Very low bit-rate video coding based on matching pursuits

We present a video compression algorithm which performs well on generic sequences at very low bit rates. This algorithm was the basis for a submission to the November 1995 MPEG-4 subjective tests. The main novelty of the algorithm is a matching-pursuit based motion residual coder. The method uses an inner-product search to decompose motion residual signals on an overcomplete dictionary of separable Gabor functions. This coding strategy allows residual bits to be concentrated in the areas where they are needed most, providing detailed reconstructions without block artifacts. Coding results from the MPEG-4 Class A compression sequences are presented and compared to H.263. We demonstrate that the matching pursuit system outperforms the H.263 standard in both peak signal-to-noise ratio (PSNR) and visual quality.