Geometric Invariant Theory based on Weil Divisors

Given an action of a reductive group on a normal variety, we construct all invariant open subsets admitting a good quotient with a quasiprojective or a divisorial quotient space. Our approach extends known constructions like Mumford's Geometric Invariant Theory. We obtain several new Hilbert-Mumford type theorems, and we extend a projectivity criterion of Bialynicki-Birula and Swiecicka for varieties with semisimple group action from the smooth to the singular case.Comment: Final version, to appear in Compositio Mat

Optimal Error Protection of Progressively Compressed 3D Meshes

Given a number of available layers of source data and a transmission bit budget, we propose an algorithm that determines how many layers should be sent and how many protection bits should be allocated to each transmitted layer such that the expected distortion at the receiver is minimum. The algorithm is used for robust transmission of progressively compressed 3D models over a packet erasure channel. In contrast to the previous approach, which uses exhaustive search, the time complexity of our algorithm is linear in the transmission bit budget

Adaptive unicast video streaming with rateless codes and feedback.

Video streaming over the Internet and packet-based wireless networks is sensitive to packet loss, which can severely damage the quality of the received video. To protect the transmitted video data against packet loss, application-layer forward error correction (FEC) is commonly used. Typically, for a given source block, the channel code rate is fixed in advance according to an estimation of the packet loss rate. However, since network conditions are difficult to predict, determining the right amount of redundancy introduced by the channel encoder is not obvious. To address this problem, we consider a general framework where the sender applies rateless erasure coding to every source block and keeps on transmitting the encoded symbols until it receives an acknowledgment from the receiver indicating that the block was decoded successfully. Within this framework, we design transmission strategies that aim at minimizing the expected bandwidth usage while ensuring successful decoding subject to an upper bound on the packet loss rate. In real simulations over the Internet, our solution outperformed standard FEC and hybrid ARQ approaches. For the QCIF Foreman sequence compressed with the H.264 video coder, the gain in average peak signal to noise ratio over the best previous scheme exceeded 3.5 decibels at 90 kilobits per second.DFG (German Research Foundation

Robust live unicast video streaming with rateless codes

"This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.""©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE."We consider live unicast video streaming over a packet erasure channel. To protect the transmitted data, previous solutions use forward error correction (FEC), where the channel code rate is fixed in advance according to an estimation of the packet loss rate. However, these solutions are inefficient under dynamic and unpredictable channel conditions because of the mismatch between the estimated packet loss rate and the actual one.We introduce a new approach based on rateless codes and receiver feedback. For every source block, the sender keeps on transmitting the encoded symbols until it receives an acknowledgment from the receiver indicating that the block was decoded successfully. Within this framework, we provide an efficient algorithm to minimize bandwidth usage while ensuring successful decoding subject to an upper bound on the packet loss rate. Experimental results showed that compared to traditional fixed-rate FEC, our scheme provides significant bandwidth savings for the same playback qualityThis work was supported by the DFG Research Training Group GK-1042

SUR-Net: Predicting the Satisfied User Ratio Curve for Image Compression with Deep Learning

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The Satisfied User Ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the probability distribution of the Just Noticeable Difference (JND) level, the smallest distortion level that can be perceived by a subject. We propose the first deep learning approach to predict such SUR curves. Instead of the direct approach of regressing the SUR curve itself for a given reference image, our model is trained on pairs of images, original and compressed. Relying on a Siamese Convolutional Neural Network (CNN), feature pooling, a fully connected regression-head, and transfer learning, we achieved a good prediction performance. Experiments on the MCL-JCI dataset showed a mean Bhattacharyya distance between the predicted and the original JND distributions of only 0.072

Optimal packet loss protection of progressively compressed 3D meshes

©20009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.We consider a state of the art system that uses layered source coding and forward error correction with Reed- Solomon codes to efficiently transmit 3D meshes over lossy packet networks. Given a transmission bit budget, the performance of this system can be optimized by determining how many layers should be sent, how each layer should be packetized, and how many parity bits should be allocated to each layer such that the expected distortion at the receiver is minimum. The previous solution for this optimization problem uses exhaustive search, which is not feasible when the transmission bit budget is large.We propose instead an exact algorithm that solves this optimization problem in linear time and space. We illustrate the advantages of our approach by providing experimental results for the CPM (Compressed Progressive Meshes) mesh compression techniqueDFG Research Training Group GK-1042

Two-Dimensional Convolutional Recurrent Neural Networks for Speech Activity Detection

Speech Activity Detection (SAD) plays an important role in mobile communications and automatic speech recognition (ASR). Developing efficient SAD systems for real-world applications is a challenging task due to the presence of noise. We propose a new approach to SAD where we treat it as a two-dimensional multilabel image classification problem. To classify the audio segments, we compute their Short-time Fourier Transform spectrograms and classify them with a Convolutional Recurrent Neural Network (CRNN), traditionally used in image recognition. Our CRNN uses a sigmoid activation function, max-pooling in the frequency domain, and a convolutional operation as a moving average filter to remove misclassified spikes. On the development set of Task 1 of the 2019 Fearless Steps Challenge, our system achieved a decision cost function (DCF) of 2.89%, a 66.4% improvement over the baseline. Moreover, it achieved a DCF score of 3.318% on the evaluation dataset of the challenge, ranking first among all submissions

Transport and MAC cross-layer protocol for video surveillance over WIMAX

Video surveillance is an emerging application for activity and security monitoring. Outdoor surveillance applications can take advantage of a WiMAX network to provide installation flexibility and mobility. A WiMAX-based surveillance system can be implemented as a dedicated network which only serves surveillance nodes to ensure high reliability. However, wireless video transmission is prone to interferences which degrade video quality. This paper proposes a novel transport and MAC cross-layer (TMC) protocol which aims at reducing delay and increasing video quality by integrating a transport layer protocol and bandwidth allocation within WiMAX. The simulations show that the proposed protocol outperforms existing protocol

GQE-Net: A Graph-based Quality Enhancement Network for Point Cloud Color Attribute

In recent years, point clouds have become increasingly popular for representing three-dimensional (3D) visual objects and scenes. To efficiently store and transmit point clouds, compression methods have been developed, but they often result in a degradation of quality. To reduce color distortion in point clouds, we propose a graph-based quality enhancement network (GQE-Net) that uses geometry information as an auxiliary input and graph convolution blocks to extract local features efficiently. Specifically, we use a parallel-serial graph attention module with a multi-head graph attention mechanism to focus on important points or features and help them fuse together. Additionally, we design a feature refinement module that takes into account the normals and geometry distance between points. To work within the limitations of GPU memory capacity, the distorted point cloud is divided into overlap-allowed 3D patches, which are sent to GQE-Net for quality enhancement. To account for differences in data distribution among different color omponents, three models are trained for the three color components. Experimental results show that our method achieves state-of-the-art performance. For example, when implementing GQE-Net on the recent G-PCC coding standard test model, 0.43 dB, 0.25 dB, and 0.36 dB Bjontegaard delta (BD)-peak-signal-to-noise ratio (PSNR), corresponding to 14.0%, 9.3%, and 14.5% BD-rate savings can be achieved on dense point clouds for the Y, Cb, and Cr components, respectively.Comment: 13 pages, 11 figures, submitted to IEEE TI

Standalone closed-form formula for the throughput rate of asynchronous normally distributed serial flow lines

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Flexible flow lines use flexible entities to generate multiple product variants using the same serial routing. Evaluative analytical models for the throughput rate of asynchronous serial flow lines were mainly developed for the Markovian case where processing times, arrival rates, failure rates and setup times follow deterministic, exponential or phase-type distributions. Models for non-Markovian processes are non-standalone and were obtained by extending the exponential case. This limits the suitability of existing models for real-world human-dependent flow lines, which are typically represented by a normal distribution. We exploit data mining and simulation modelling to derive a standalone closed-form formula for the throughput rate of normally distributed asynchronous human-dependent serial flow lines. Our formula gave steady results that are more accurate than those obtained with existing models across a wide range of discrete data sets