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Conference

ACM SIGMM Conference on Multimedia Systems 

About: ACM SIGMM Conference on Multimedia Systems is an academic conference. The conference publishes majorly in the area(s): Computer science & Quality of experience. Over the lifetime, 487 publications have been published by the conference receiving 12552 citations.

Papers published on a yearly basis

Papers
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Proceedings ArticleDOI
23 Feb 2011
TL;DR: In this paper, some insight and background into the Dynamic Adaptive Streaming over HTTP (DASH) specifications as available from 3GPP and in draft version also from MPEG is provided.
Abstract: In this paper, we provide some insight and background into the Dynamic Adaptive Streaming over HTTP (DASH) specifications as available from 3GPP and in draft version also from MPEG. Specifically, the 3GPP version provides a normative description of a Media Presentation, the formats of a Segment, and the delivery protocol. In addition, it adds an informative description on how a DASH Client may use the provided information to establish a streaming service for the user. The solution supports different service types (e.g., On-Demand, Live, Time-Shift Viewing), different features (e.g., adaptive bitrate switching, multiple language support, ad insertion, trick modes, DRM) and different deployment options. Design principles and examples are provided.

1,203 citations

Proceedings ArticleDOI
23 Feb 2011
TL;DR: This paper focuses on the rate-adaptation mechanisms of adaptive streaming and experimentally evaluates two major commercial players (Smooth Streaming, Netflix) and one open source player (OSMF).
Abstract: Adaptive (video) streaming over HTTP is gradually being adopted, as it offers significant advantages in terms of both user-perceived quality and resource utilization for content and network service providers. In this paper, we focus on the rate-adaptation mechanisms of adaptive streaming and experimentally evaluate two major commercial players (Smooth Streaming, Netflix) and one open source player (OSMF). Our experiments cover three important operating conditions. First, how does an adaptive video player react to either persistent or short-term changes in the underlying network available bandwidth. Can the player quickly converge to the maximum sustainable bitrate? Second, what happens when two adaptive video players compete for available bandwidth in the bottleneck link? Can they share the resources in a stable and fair manner? And third, how does adaptive streaming perform with live content? Is the player able to sustain a short playback delay? We identify major differences between the three players, and significant inefficiencies in each of them.

729 citations

Proceedings ArticleDOI
23 Feb 2011
TL;DR: A receiver-driven rate adaptation method for HTTP/TCP streaming that deploys a step-wise increase/ aggressive decrease method to switch up/down between the different representations of the content that are encoded at different bitrates is presented.
Abstract: Recently, HTTP has been widely used for the delivery of real-time multimedia content over the Internet, such as in video streaming applications. To combat the varying network resources of the Internet, rate adaptation is used to adapt the transmission rate to the varying network capacity. A key research problem of rate adaptation is to identify network congestion early enough and to probe the spare network capacity. In adaptive HTTP streaming, this problem becomes challenging because of the difficulties in differentiating between the short-term throughput variations, incurred by the TCP congestion control, and the throughput changes due to more persistent bandwidth changes.In this paper, we propose a novel rate adaptation algorithm for adaptive HTTP streaming that detects bandwidth changes using a smoothed HTTP throughput measured based on the segment fetch time (SFT). The smoothed HTTP throughput instead of the instantaneous TCP transmission rate is used to determine if the bitrate of the current media matches the end-to-end network bandwidth capacity. Based on the smoothed throughput measurement, this paper presents a receiver-driven rate adaptation method for HTTP/TCP streaming that deploys a step-wise increase/ aggressive decrease method to switch up/down between the different representations of the content that are encoded at different bitrates. Our rate adaptation method does not require any transport layer information such as round trip time (RTT) and packet loss rates which are available at the TCP layer. Simulation results show that the proposed rate adaptation algorithm quickly adapts to match the end-to-end network capacity and also effectively controls buffer underflow and overflow.

455 citations

Proceedings ArticleDOI
18 Mar 2015
TL;DR: How RAISE has been collected and organized is described, how digital image forensics and many other multimedia research areas may benefit of this new publicly available benchmark dataset and a very recent forensic technique for JPEG compression detection is tested.
Abstract: Digital forensics is a relatively new research area which aims at authenticating digital media by detecting possible digital forgeries. Indeed, the ever increasing availability of multimedia data on the web, coupled with the great advances reached by computer graphical tools, makes the modification of an image and the creation of visually compelling forgeries an easy task for any user. This in turns creates the need of reliable tools to validate the trustworthiness of the represented information. In such a context, we present here RAISE, a large dataset of 8156 high-resolution raw images, depicting various subjects and scenarios, properly annotated and available together with accompanying metadata. Such a wide collection of untouched and diverse data is intended to become a powerful resource for, but not limited to, forensic researchers by providing a common benchmark for a fair comparison, testing and evaluation of existing and next generation forensic algorithms. In this paper we describe how RAISE has been collected and organized, discuss how digital image forensics and many other multimedia research areas may benefit of this new publicly available benchmark dataset and test a very recent forensic technique for JPEG compression detection.

440 citations

Proceedings ArticleDOI
20 Jun 2017
TL;DR: KVASIR is a dataset containing images from inside the gastrointestinal (GI) tract that contains two categories of images related to endoscopic polyp removal and is important for research on both single and multi-disease computer aided detection.
Abstract: Automatic detection of diseases by use of computers is an important, but still unexplored field of research. Such innovations may improve medical practice and refine health care systems all over the world. However, datasets containing medical images are hardly available, making reproducibility and comparison of approaches almost impossible. In this paper, we present KVASIR, a dataset containing images from inside the gastrointestinal (GI) tract. The collection of images are classified into three important anatomical landmarks and three clinically significant findings. In addition, it contains two categories of images related to endoscopic polyp removal. Sorting and annotation of the dataset is performed by medical doctors (experienced endoscopists). In this respect, KVASIR is important for research on both single- and multi-disease computer aided detection. By providing it, we invite and enable multimedia researcher into the medical domain of detection and retrieval.

351 citations

Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
202356
202253
202149
202048
201940
201869