nils-herrmann/clean_scirepeval huggingface dataset:
H.264-based stream morphing with scalable motion coding

Stream morphing is a technique that allows for two or more video bitstreams coded from the same original material with different quality settings to be simulcast in an efficient way. This paper describes our second generation stream morphing system that is based on the H.264 standard rather than MPEG-4 as used in our original implementation. While the basic operation of stream morphing remains largely unchanged for H.264, a number of enhancements have been made. Firstly, enhancement layers are coded using the same low-complexity, adaptive arithmetic coder utilized by H.264 itself. Furthermore, a scalable motion model has been added which allows for the use of very low bit rates in the base layer compared to the previous approach that used the same motion vectors and macroblock partitioning modes in all layers.
Threshold-Based Image Segmentation through an Improved Particle Swarm Optimisation

Image segmentation plays a critical role in the process of object recognition in digital image processing. Multilevel threshold-based segmentation is one of the most popular image segmentation techniques. This paper proposes a new multilevel threshold-based image segmentation approach on the basis of the new particle swarm optimisation with wavelet mutation. Unlike the conventional particle swarm optimisation (PSO), our new algorithm distinguishes itself by having the following advantages: 1) Faster convergence rate; 2) Multi-dimensional data processing. The basic idea in this paper is to optimise the multilevel thresholds for the images and therefore to reach the goal of the total entropy of the image being maximized. The new algorithm leads to better optimised thresholds and produces more accurate segmentation results for images with multiple attributes. The trade-off between good search stability and cheaper computational cost is well balanced. By comparing with the Genetic Algorithm(GA) and an existing PSO-based image scheme called HCOCLPSO, the experimental results demonstrate the proposed scheme outperforms and very promising in terms of faster convergence rate, better exploration and exploitation capacities in the segmentation process with the reduced computational time.
MPEG 2 Video Services for Wireless ATM Networks

Audio-visual and other multimedia services are seen as an important source of traffic for future telecommunications networks, including wireless networks. We examine the impact of the properties of a 50 Mb/s asynchronous transfer mode (ATM)-based wireless local-area network (WLAN) on Moving Picture Experts Group phase 2 (MPEG 2) compressed video traffic, with emphasis on the network's error characteristics. The paper includes a description of the WLAN system used and its loss characteristics, a brief discussion of relevant aspects of the MPEG 2 standards and the associated error resilience techniques for minimizing the effect of transmission errors, and a description of the method by which the video data is organized for transmission on the network. We show results on the effect of cell loss due to transmission errors on the quality of the decoded video at the receiver, and demonstrate how error resilience techniques in both the systems and video layers of MPEG 2 can be used to improve the quality of service. Situations where up to 1% of the data is lost due to network transmission errors are examined. Most important among the findings are that error resilience experiments that do not take into account the effect of the MPEG 2 systems layer will tend to significantly overestimate the quality of received video, and that the error resilience techniques provided within the MPEG 2 standard are not sufficient to provide acceptable quality with acceptable overheads, but that this quality can be significantly increased by the addition of a small number of simple techniques.
Low Precision Global Motion Estimation for Video Compression- A Generalized Framework

Global motion estimation (GME) is a fundamental problem in video compression and has been one of the most complex algorithms in digital video processing. The efficiency of real-time video processing operations has an important impact on the cost and realizability of complex algorithms, such as global motion estimation. Most digital video processing is carried out with a precision of 8 bits per pixel, however there has always been interest in low-complexity algorithms. One way of achieving low-complexity is through low precision, such as might be achieved by quantization of each pixel to a single bit. Previous approaches to one-bit motion estimation have achieved quantization through a combination of spatial filtering/averaging and threshold setting. In this paper we present a generalized framework for precision reduction in video compression. Motivated by this generalized framework, we show that bit-plane selection provides higher performance, with lower complexity, than conventional approaches to quantization.
A computationally efficient approach for 2D-3D image registration

2D-3D image registration has become an important tool in many clinical applications such as image-guided surgery and the kinematic analysis of bones in knee and ankle joints. A limitation of this approach is the need to recalculate the voxel values in the 3D volume for every iteration of the registration procedure. In this paper we propose a new 2D-3D image registration algorithm which uses the projected 2D data from the original 3D CT volume. For the majority of the iterations of the algorithm, only this 2D data is updated rather than the 3D volume. Experimental results show that similar registration accuracy to the approach which employs 3-D updates at every iteration can be achieved with our method if we employ 3-D updates only in the last few iterations. As a result of reducing the number of 3-D updates, the proposed approach reduces the time required to perform the registration by approximately a factor of five.
Local minima escape transients of CMA

We examine the implications of our recent analysis of the local minima escape behavior of the Godard (or CMA) blind equalizer. This analysis suggests asymptotic estimates for the expected value of the escape time when leaving the region of attraction of local equilibria. For a particular source, channel, and equalizer local minimum parameterization, the log of the inverse of the mean escape time is estimated to be proportional to a weighted sum of the stepsize and the channel noise variance. We examine the validity of the functional form of this relationship for two examples. This experimental evidence corroborates our earlier analysis and suggests that the mean time to escape can be very large for the Godard (1980) algorithm (also known as the constant modulus algorithm).<<ETX>>
Identification of neighbors in a long delay network using un-slotted and slotted protocols

Identification of neighbor (nodes which are within the maximum transmission range of a given node) in a wireless network is helpful in the preliminary stage of the network or in later stages in order to route or broadcast information throughout the network. Recently a number of identification procedures have been proposed for radio frequency identification (RFID) systems, which are applicable to terrestrial communication networks using electromagnetic wave. However, if those procedures are implemented in a long delay network (such as an underwater acoustic network), then a large time will be wasted due to the propagation delay. Here we propose two methods for identifying neighbors in a long delay network either un-slotted or slotted protocols. The first procedure is based on previous approaches in RFID systems with adjustment for a long delay network. The second is an improvement on the first one using an optimum number of probes. The second procedure outperforms the first with 75% improvement in time and 60% improvement with power consumption when an estimation of the number of neighbors is known earlier.
Dense depth estimation using adaptive structured light and cooperative algorithm

In this paper we propose a new depth estimation approach using adaptive structured light. A random noise adaptive structured light pattern is projected onto objects and then two cameras capture stereo images. The adaptive colors are acquired using principle component analysis in the RGB color space of the image of the scene. By using inverse principle component analysis on the images with structured light, the desirable structured light information can be maximally retrieved. By combining the original three RGB channels of the scene under adaptive structured light with a fourth channel generated using inverse principle component analysis we can use the cooperative algorithm to generate a dense depth map. In order to keep clear depth discontinuities and alleviate noise in the depth map, we aggregate the local match score with shiftable windows. Experimental results show our approach performs well on images of real-world objects with strong colors and complex textures that have been captured under ambient light conditions.
An Adaptive Propagation-delay-tolerant MAC Protocol for Underwater Acoustic Sensor Networks

To support the retrieval of accurate and up-to-date information from beneath the world's oceans, it is desirable to deploy underwater sensor networks. However, since radio frequencies (RF) do not propagate well underwater, acoustic channels are employed to support long-distance and low-power communication in underwater sensor networks even though acoustic signals suffer from long propagation delay and have very limited bandwidth. In this paper, we first show the limitation of conventional MAC protocols in the proposed environment; and introduce a new MAC protocol: the adaptive propagation-delay-tolerant collision-avoidance protocol (APCAP). We demonstrate that, in channels with long propagation delay, APCAP significantly outperforms those MAC protocols that are devised for RF-based networks.
An Efficient Mode Selection Prior to the Actual Encoding for H.264/AVC Encoder

Many video compression algorithms require decisions to be made to select between different coding modes. In the case of H.264, this includes decisions about whether or not motion compensation is used, and the block size to be used for motion compensation. It has been proposed that constrained optimization techniques, such as the method of Lagrange multipliers, can be used to trade off between the quality of the compressed video and the bit rate generated. In this paper, we show that in many cases of practical interest, very similar results can be achieved with much simpler optimizations. Mode selection by simply minimizing the distortion with motion vectors and header information produces very similar performance to the full constrained optimization, while it reduces the mode selection and over all encoding time by 31% and 12%, respectively. The proposed approach can be applied together with fast motion search algorithms and the mode filtering algorithms for further speed up.
Scalable video coding by stream morphing

In many scenarios where it is desirable to provide a number of different descriptions of a video sequence, the simulcasting of single-layer bitstreams is impractical due to the amount of data involved. The new technique "stream morphing" introduced in this paper exploits the redundancy that exists between different compressed descriptions of the same video sequence at different quality levels. This works to dramatically reduce the data rates required to achieve the same effect as simulcast. The results shown here indicate that an SNR scalable video system constructed using this principle outperforms the structures defined in MPEG-2 and the MPEG-4 Streaming Video Profile. Stream morphing has a number of additional advantages: no loss of subjective quality compared to single-layer bitstreams at the same peak SNR regardless of the number of layers involved (unlike the MPEG-2 approach), the ability to generate a scalable representation of a video sequence from a set of single-layer descriptions in a computationally inexpensive way, and compatibility with existing single-layer decoders via a simple preprocessor without the need for transcoding. Stream morphing is demonstrated here in the context of MPEG-4; however, it is compatible with other similar video coding standards.
Improved Image Registration using the Up-Sampled Domain

We consider the warping problem which appears in well-known image registration algorithms that use higher-order motion models. An implementation inspired by recent work and a new image registration algorithm are used for the analysis. Both approaches rely on frame-to-frame estimation. The key technique is the well-established gradient descent approach for the estimation of higher-order motion parameters. We show that using up-sampled input images in the last step of the algorithms improves the accuracy of the estimated motion parameters. It can be seen in the experimental results that the performance of the image registration algorithms increases significantly only by applying the gradient descent on up-sampled images in comparison to recent algorithms developed
Emulating radio network path losses with hardware hub

This paper introduces a framework to emulate the path loss of radio network links in a laboratory environment. The emulator network comprises one or more multiport radio frequency (RF) hubs with variable attenuation on each port to be able to emulate the radio path loss between any two radios. The problem in this emulation approach is the determination of the appropriate port attenuation to match the multi-dimensional link losses (signal attenuations) and signal-to-interference ratios between multiple radios of a practical network. This has been structured as a constraint satisfaction problem (CSP) for both single-hub and multiple-hub emulations. We describe an algorithm based on Freuder's solution synthesis to find the solution to the CSP. Experimental results are also presented to validate the accuracy of emulation.
Extended motion compensation using larger blocks and an elastic motion model

Motion estimation and compensation contribute greatly to video compression efficiency. Previous work has explored the effects of higher-order motion models with standard block sizes on video coding, but the impact on compression efficiency has not been sufficient to see such approaches adopted in mainstream video-compression standards. In this paper, we put forward a new strategy to augment the performance of extended motion models in the perspective of classical block-matching algorithms. To accomplish superior compression, the proposed scheme takes advantage of larger blocks with multi-level partitioning and a higher order elastic motion model. Performance evaluations show that these two features, when combined under a common framework, can significantly outperform the existing techniques including those available in the recent H.264 standard.
Estimation in Underwater Sensor Networks Taking into Account Capture

Like any other ad-hoc network, an underwater communication network (UWCN) is a collection of nodes (we have assumed a large number), each of which is initially unaware of the existence of other nodes. Often, it is desirable for each node to be able to estimate the number of neighbours (i.e., the number of directly visible nodes) with which it can communicate. This paper provides a probabilistic estimation procedure for counting the number of neighbours for an UWCN (as well as for any wireless ad hoc networks) taking into account capture effect. Using the same procedure we are also able to estimate the distribution of nodes, i.e. their dimensionality.
A Low Complexity Algorithm for Global Motion Parameter Estimation Targeting Hardware Implementation

Now-a-days image alignment is one of the most widely used techniques in computer vision. Image alignment has many applications in fields as diverse as video surveillance, computer vision, medical imaging, and video coding. The estimation of an objects’ motion is a key step in image alignment. In this paper, we will present a low-complexity algorithm for estimation of motion parameters. Most of the motion parameters estimation algorithms are carried out with a precision of 8 bits per pixel; here we propose an algorithm using only 1 bit per pixel resulting in lower complexity. The proposed method includes a technique for calculating the gradient of the sum-of-squared difference (SSD) using XOR operations instead of multiplication. Experimental results show that the proposed method compares favorably with registration using the full precision of the input images.
An immunology-inspired multi-engine anomaly detection system with hybrid particle swarm optimisations

In this paper, multiple detection engines with multi-layered intrusion detection mechanisms are proposed for enhancing computer security. The principle is to coordinate the results from each single-engine intrusion alert system, which seamlessly integrates with a multiple layered distributed service-oriented structure. An improved hidden Markov model (HMM) is created for the detection engine which is capable of the immunology-based self/nonself discrimination. The classifications of normal and abnormal behaviours of system calls are further examined by an advanced fuzzy-based inference process tuned by HPSOWM. Considering a real benchmark dataset from the public domain, our experimental results show that the proposed scheme can greatly shorten the training time of HMM and significantly reduce the false positive rate. The proposed HPSOWM works especially well for the efficient classification of unknown behaviors and malicious attacks.
Food Volume Estimation in a Mobile Phone Based Dietary Assessment System

There is now convincing evidence that poor diet, in combination with physical inactivity are key determinants of an individual's risk of developing chronic diseases, such as obesity, cancer, cardiovascular disease or diabetes. Assessing what people eat is fundamental to establishing the link between diet and disease. Food records are considered the best approach for assessing energy intake. However, this method requires literate and highly motivated subjects and adolescents and young adults are the least likely to undertake food records. The ready access of the majority of the population to mobile phones has opened up new opportunities for dietary assessment. In such systems, the camera in the mobile phone is used for capturing images of food consumed and these images are then processed to automatically estimate the nutritional content of the food. A vital step in this process is the estimation of the volume of the food in the image. In this paper we propose a food volume estimation approach which requires only a pair of stereo images to be captured. Our experimental results show that the proposed approach can provide an accurate estimate of the volume of typical food items in a passive manner without the need for manual fitting of 3D models to the food items.
Tile-boundary artifact reduction using odd tile size and the low-pass first convention

It is well known that tile-boundary artifacts occur in wavelet-based lossy image coding. However, until now, their cause has not been understood well. In this paper, we show that boundary artifacts are an inescapable consequence of the usual methods used to choose tile size and the type of symmetric extension employed in a wavelet-based image decomposition system. This paper presents a novel method for reducing these tile-boundary artifacts. The method employs odd tile sizes (2/sup N/+1 samples) rather than the conventional even tile sizes (2/sup N/ samples). It is shown that, for the same bit rate, an image compressed using an odd tile length low-pass first (OTLPF) convention has significantly less boundary artifacts than an image compressed using even tile sizes. The OTLPF convention can also be incorporated into the JPEG 2000 image compression algorithm using extensions defined in Part 2 of this standard.
Recurrence times of buffer overflows in Jackson networks

The estimation of the statistics of buffer overflows in networks of queues by means of simulation is inherently costly, simply because of the rarity of these events. An alternative analytic approach is presented, with very low computational cost, for calculating the recurrence time of buffer overflows for Jackson networks in which the recurrence times of buffer overflows in a network are expressed in terms of the recurrence times for overflows of individual buffers, isolated from the network. This result is applied to the buffer allocation problem for queueing networks, providing extensions and further justification for a previously derived heuristic approach to this problem.
Implications of simplifying SINR in underwater acoustic networks

Link analysis in a network must consider the effects of both noise and interference on signal quality. Because absorption and noise are frequency dependant the levels of a signal, interference and noise vary across the channel bandwidth. Previous analysis of underwater acoustic link capacity addressed signal-to-noise ratio across the signal bandwidth, we extend such analysis to include interference. At high frequencies links are limited by low signal levels that result from absorption, but at low frequencies such links are more usually limited by interference. As a result, transmitter power could be reallocated from low frequencies to high frequencies to improve overall throughput.
Spatial temporal concealment of lost blocks in coded video

When errors are introduced into a compressed video bit-stream, blocks of pixels are lost. The decoder attempts to conceal this loss to maximize the displayed picture quality. Previous approaches use either surrounding pixels in the current picture to conceal lost blocks (spatial concealment) or pixels from the previous picture (temporal concealment). We show how joint spatial temporal concealment can be implemented to provide higher quality concealment. To do that we model video sequences as mixtures of simple Markov processes and utilise the model for concealment of portions of video which are missing. This model allows us to combine spatial and temporal information in a seamless manner when performing concealment on the missing blocks.
Scale and Rotation Invariant Gabor Features for Texture Retrieval

For image classification applications it is often useful to generate a compact representation of the texture of an image region. The conventional representation of image textures using extracted Gabor wavelet coefficients often yields poor performance when classifying scaled and rotated versions of image regions. In this paper we propose a scale and rotation invariant feature generation procedure for classification and of images using Gabor filter banks. Firstly, to obtain scale and rotation invariant features, each image is decomposed at different scales and orientations. Then, in order to create unique feature vectors, we apply a circular shift operation to both scale and rotation dimensions to shift the maximum value of the Gabor filters to the first orientation of the first scale and the energies of these filtered images are calculated. To demonstrate the effectiveness of our proposed approach we compare its performance with the most recent texture feature generation methods in a classification task. Experimental results show that our proposed feature generation method is more accurate at classifying scaled and rotated textures than the existing methods.