1,721,084 research outputs found
Distributed Scheduling for Low-Delay and Loss-Resilient Media Streaming with Network Coding
Full text linkNetwork coding (NC) has been shown to be very effective for collaborative media streaming applications. A pivotal issue in media streaming with NC lies in the packet scheduling policy at the network nodes, which affects the perceived media quality. In this paper, we address the problem of finding the packet scheduling policy that maximizes the number of media segments recovered in the network. We cast this as a distributed minimization problem and propose heuristic solutions that make the proposed framework robust to infrequent or inaccurate feedback information. Moreover, the proposed framework accounts for the properties of layered and multiple description encoded media to provide graceful quality degradation in case of packet losses or lack of upload bandwidth. Experimental results on a local testbed as well as PlanetLab suggest that our scheduling framework achieves better media quality, lower playback delay, and lower bandwidth consumption than a random-push scheme
Securing Network Coding Architectures against Pollution Attacks with Band Codes
Full text linkDuring a pollution attack, malicious nodes purposely transmit bogus data to the honest nodes to cripple the communication. Securing the communication requires identifying and isolating the malicious nodes. However, in network coding (NC) architectures, random recombinations at the nodes increase the probability that honest nodes relay polluted packets. Thus, discriminating between honest and malicious nodes to isolate the latter turns out to be challenging at best. Band codes (BCs) are a family of rateless codes whose coding window size can be adjusted to reduce the probability that honest nodes relay polluted packets. We leverage such a property to design a distributed scheme for identifying the malicious nodes in the network. Each node counts the number of times that each neighbor has been involved in cases of polluted data reception and exchanges such counts with its neighbor nodes. Then, each node computes for each neighbor a discriminative honest score estimating the probability that the neighbor relays clean packets. We model such probability as a function of the BC coding window size, showing its impact on the accuracy and effectiveness of our distributed blacklisting scheme. We experiment distributing a live video feed in a P2P NC system, verifying the accuracy of our model and showing that our scheme allows us to secure the network against pollution attacks recovering near pre-attack video quality
METHOD FOR COMPRESSING A SEQUENCE OF IMAGES DISPLAYING SYNTHETIC GRAPHICAL ELEMENTS OF NON-PHOTOGRAPHIC ORIGIN
No full textMethod for compressing a sequence of images comprising a first image and a second image, the method comprising the steps of: generating (102) a first descriptor comprising parameters for displaying a computer-generated graphical element in the first image, the graphical element being of non-photographic origin, and the display parameters not comprising pixel values; processing the second image so as to determine (208) an event which gave rise to a potential variation in the parameters for displaying the graphical element between the first image and the second image; generating (210) a second descriptor comprising an event code indicating the determined event
Shot-based object retrieval from video with compressed Fisher vectors
Full text linkThis paper addresses the problem of retrieving those shots from a database of video sequences that match a query image. Existing architectures are mainly based on Bag of Words model, which consists in matching the query image with a high-level representation of local features extracted from the video database. Such architectures lack however the capability to scale up to very large databases. Recently, Fisher Vectors showed promising results in large scale image retrieval problems, but it is still not clear how they can be best exploited in video-related applications. In our work, we use compressed Fisher Vectors to represent the video-shots and we show that inherent correlation between video-frames can be proficiently exploited. Experiments show that our proposal enables better performance for lower computational requirements than similar architectures
Simple countermeasures to mitigate the effect of pollution attack in network coding-based peer-to-peer live streaming
Full text linkNetwork coding (NC)-based peer-to-peer (P2P) streaming represents an effective solution to aggregate user capacities and to increase system throughput in live multimedia streaming. Nonetheless, such systems are vulnerable to pollution attacks where a handful of malicious peers can disrupt the communication by transmitting just a few bogus packets which are then recombined and relayed by unaware honest nodes, further spreading the pollution over the network. Whereas previous research focused on malicious nodes identification schemes and pollution-resilient coding, in this paper we show pollution countermeasures which make a standard NC scheme resilient to pollution attacks. Thanks to a simple yet effective analytical model of a reference node collecting packets by malicious and honest neighbors, we demonstrate that: i) packets received earlier are less likely to be polluted, and ii) short generations increase the likelihood to recover a clean generation. Therefore, we propose a recombination scheme where nodes draw packets to be recombined according to their age in the input queue, paired with a decoding scheme able to detect the reception of polluted packets early in the decoding process and short generations. The effectiveness of our approach is experimentally evaluated in a real system we developed and deployed on hundreds to thousands of peers. Experimental evidence shows that, thanks to our simple countermeasures, the effect of a pollution attack is almost canceled and the video quality experienced by the peers is comparable to pre-attack levels
Vehicle joint make and model recognition with multiscale attention windows
No full textVehicle Make and Model Recognition (VMMR) deals with the problem of classifying vehicles whose appearance may vary significantly when captured from different perspectives. A number of successful approaches to this problem rely on part-based models, requiring however labor-intensive parts annotations. In this work, we address the VMMR problem proposing a deep convolutional architecture built upon multi-scale attention windows. The proposed architecture classifies a vehicle over attention windows which are predicted to minimize the classification error. Through these windows, the visual representations of the most discriminative part of the vehicle are aggregated over different scales which in fact provide more representative features for the classifier. In addition, we define a loss function accounting for the joint classification error across make and model. Besides, a training methodology is devised to stabilize the training process and to impose multi-scale constraints on predicted attention windows. The proposed architecture outperforms state-of-the-art schemes reducing the model classification error over the Stanford dataset by 1.7 % and improving the classification accuracy by 0.2 % and 0.3 % on model and make respectively over the CompCar dataset
METHOD FOR IMAGE PROCESSING AND APPARATUS FOR IMPLEMENTING THE SAME
No full textA method of processing a first image in a first plurality of images, wherein the first image is divided into a plurality of pixel blocks, is proposed, which comprises, for a current block of the first image: selecting, in a set of a plurality of predefined interpolation filters, an interpolation filter based on a prediction of an interpolation filter determined by a supervised learning algorithm to which data related to the current block is input; and using the selected interpolation filter for calculating fractional pixel values in a second image of the plurality of images for a temporal prediction of pixels of the current block based on a reference block correlated to the current block in the second image, wherein the second image is distinct from the first image and was previously encoded according to an image encoding sequence for encoding the images of the plurality of images
Robust and efficient airplane cockpit video coding leveraging temporal redundancy
Full text linkAirplane cockpit screens consist of virtual instruments where characters, numbers, and graphics are overlaid on a black or natural background. Recording the cockpit screen allows one to log vital plane data, as aircraft manufacturers do not offer direct access to raw data. However, traditional video codecs struggle at preserving character readability at the required low bit-rates. We showed in a previous work that large rate-distortion gains can be achieved if the characters are encoded as text rather than as pixels. We now leverage temporal redundancy to both achieve robust character recognition and improve encoding efficiency. A convolutional neural network is trained for character classification over synthetic samples augmented with occlusions to gain robustness against overlapping graphics. Further robustness to background occlusions is brought by a probabilistic framework that error-corrects the output of the convolutional neural network. Next, we propose a predictive text coding technique specifically tailored for text in cockpit videos that achieves competitive performance over commodity lossless methods. Experiments with real cockpit video footage show large rate-distortion gains for the proposed method with respect to three different video compression standards. Notably, the H.264/AVC codec retrofitted with our method outperforms H.265/HEVC-SCC and is competitive with the much more complex H.266/VVC while preserving text and graphics. The entire pipeline described in this work has been implemented at Safran Electronics as an embedded avionics system drawing just 2W of power thanks to a combination of software and FPGA implementation
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