No Arabic abstract
Due to the advances in hardware technology and increase in production of multimedia data in many applications, during the last decades, multimedia databases have become increasingly important. Contentbased multimedia retrieval is one of an important research area in the field of multimedia databases. Lots of research on this field has led to proposition of different kinds of index structures to support fast and efficient similarity search to retrieve multimedia data from these databases. Due to variety and plenty of proposed index structures, we suggest a systematic framework based on partitioning method used in these structures to classify multimedia index structures, and then we evaluated these structures based on important functional measures. We hope this proposed framework will lead to empirical and technical comparison of multimedia index structures and development of more efficient structures at future.
Online social networking techniques and large-scale multimedia systems are developing rapidly, which not only has brought great convenience to our daily life, but generated, collected, and stored large-scale multimedia data. This trend has put forward higher requirements and greater challenges on massive multimedia data retrieval. In this paper, we investigate the problem of image similarity measurement which is used to lots of applications. At first we propose the definition of similarity measurement of images and the related notions. Based on it we present a novel basic method of similarity measurement named SMIN. To improve the performance of calculation, we propose a novel indexing structure called SMI Temp Index (SMII for short). Besides, we establish an index of potential similar visual words off-line to solve to problem that the index cannot be reused. Experimental evaluations on two real image datasets demonstrate that our solution outperforms state-of-the-art method.
Fixed infrastructured networks naturally support centralized approaches for group management and information provisioning. Contrary to infrastructured networks, in multi-hop ad-hoc networks each node acts as a router as well as sender and receiver. Some applications, however, requires hierarchical arrangements that-for practical reasons-has to be done locally and self-organized. An additional challenge is to deal with mobility that causes permanent network partitioning and re-organizations. Technically, these problems can be tackled by providing additional uplinks to a backbone network, which can be used to access resources in the Internet as well as to inter-link multiple ad-hoc network partitions, creating a hybrid wireless network. In this paper, we present a prototypically implemented hybrid wireless network system optimized for multimedia content distribution. To efficiently manage the ad-hoc communicating devices a weighted clustering algorithm is introduced. The proposed localized algorithm deals with mobility, but does not require geographical information or distances.
This paper provides a technical overview of a deep-learning-based encoder method aiming at optimizing next generation hybrid video encoders for driving the block partitioning in intra slices. An encoding approach based on Convolutional Neural Networks is explored to partly substitute classical heuristics-based encoder speed-ups by a systematic and automatic process. The solution allows controlling the trade-off between complexity and coding gains, in intra slices, with one single parameter. This algorithm was proposed at the Call for Proposals of the Joint Video Exploration Team (JVET) on video compression with capability beyond HEVC. In All Intra configuration, for a given allowed topology of splits, a speed-up of $times 2$ is obtained without BD-rate loss, or a speed-up above $times 4$ with a loss below 1% in BD-rate.
In this paper, we propose an export architecture that provides a clear separation of authoring services from publication services. We illustrate this architecture with the LimSee3 authoring tool and several standard publication formats: Timesheets, SMIL, and XHTML.
Soft-cast, a cross-layer design for wireless video transmission, is proposed to solve the drawbacks of digital video transmission: threshold transmission framework achieving the same effect. Specifically, in encoder, we carry out power allocation on the transformed coefficients and encode the coefficients based on the new formulation of power distortion. In decoder, the process of LLSE estimator is also improved. Accompanied with the inverse nonlinear transform, DCT coefficients can be recovered depending on the scaling factors , LLSE estimator coefficients and metadata. Experiment results show that our proposed framework outperforms the Soft-cast in PSNR 1.08 dB and the MSSIM gain reaches to 2.35% when transmitting under the same bandwidth and total power.