GPU-driven recombination and transformation of YCoCg-R video samples

Common programmable Graphics Processing Units (GPU) are capable of more than just rendering real-time effects for games. They can also be used for image processing and the acceleration of video decoding. This paper describes an extended implementation of the H.264/AVC YCoCg-R to RGB color space transformation on the GPU. Both the color space transformation and recombination of the color samples from a nontrivial data layout are performed by the GPU. Using mid- to high-range GPUs, this extended implementation offers a significant gain in processing speed compared to an existing basic GPU version and an optimized CPU implementation. An ATI X1900 GPU was capable of processing more than 73 high-resolution 1080p YCoCg-R frames per second, which is over twice the speed of the CPU-only transformation using a Pentium D 820

Reconstructing human-generated provenance through similarity-based clustering

In this paper, we revisit our method for reconstructing the primary sources of documents, which make up an important part of their provenance. Our method is based on the assumption that if two documents are semantically similar, there is a high chance that they also share a common source. We previously evaluated this assumption on an excerpt from a news archive, achieving 68.2% precision and 73% recall when reconstructing the primary sources of all articles. However, since we could not release this dataset to the public, it made our results hard to compare to others. In this work, we extend the flexibility of our method by adding a new parameter, and re-evaluate it on the human-generated dataset created for the 2014 Provenance Reconstruction Challenge. The extended method achieves up to 86% precision and 59% recall, and is now directly comparable to any approach that uses the same dataset

Multi-loop quality scalability based on high efficiency video coding

Scalable video coding performance largely depends on the underlying single layer coding efficiency. In this paper, the quality scalability capabilities are evaluated on a base of the new High Efficiency Video Coding (HEVC) standard under development. To enable the evaluation, a multi-loop codec has been designed using HEVC. Adaptive inter-layer prediction is realized by including the lower layer in the reference list of the enhancement layer. As a result, adaptive scalability on frame level and on prediction unit level is accomplished. Compared to single layer coding, 19.4% Bjontegaard Delta bitrate increase is measured over approximately a 30dB to 40dB PSNR range. When compared to simulcast, 20.6% bitrate reduction can be achieved. Under equivalent conditions, the presented technique achieves 43.8% bitrate reduction over Coarse Grain Scalability of the SVC - H.264/AVC-based standard

Format-independent media delivery, applied to RTP, MP4, and Ogg

The current multimedia landscape is characterized by a significant heterogeneity in terms of coding and delivery formats, usage environments, and user preferences. This paper introduces a transparent multimedia content adaptation and delivery approach, i.e., model-driven content adaptation and delivery. It is based on a model that takes into account the structural metadata, semantic metadata, and scalability information of media bitstreams. Further, a format-independent multimedia packaging method is proposed based on this model for media bitstreams and MPEG-B BSDL. Thus, multimedia packaging is obtained by encapsulating the selected and adapted structural metadata within a specific delivery format. This packaging process is implemented using XML transformation filters and MPEG-B BSDL. To illustrate this format-independent packaging technique, we apply it to three packaging formats: RTP, MP4, and Ogg

Multi-camera analysis of soccer sequences

The automatic detection of meaningful phases in a soccer game depends on the accurate localization of players and the ball at each moment. However, the automatic analysis of soccer sequences is a challenging task due to the presence of fast moving multiple objects. For this purpose, we present a multi-camera analysis system that yields the position of the ball and players on a common ground plane. The detection in each camera is based on a code-book algorithm and different features are used to classify the detected blobs. The detection results of each camera are transformed using homography to a virtual top-view of the playing field. Within this virtual top-view we merge trajectory information of the different cameras allowing to refine the found positions. In this paper, we evaluate the system on a public SOCCER dataset and end with a discussion of possible improvements of the dataset