Spectral Classified Vector Quantization (SCVQ) for Multispectral Images

Multi- and hyper-spectral data pose severe problems in terms of storage capacity and transmission bandwidth. Although recommendable, compression techniques require efficient approaches to guarantee an adequate fidelity level. In particular, depending on the final destination of the data, it could be necessary to maximize several parameters, as for instance the visual quality of the rendered data, the correctness of their interpretation, or the performance of their classification. Based on the idea of Spectral Vector Quantization, the approach proposed in this paper aims at combining a compression and a classification methodology into a single scheme, in which visual distortion and classification accuracy can be balanced a- priori according to the requirements of the target application. Experimental results demonstrate that the proposed approach can be employed successfully in a wide range of application domains

Low-Complexity Context-Based Motion Compensation for VLBR Video Encoding

A significant improvement of block-based motion estimation strategies is presented, which provides fast computation and very low bitrate coding. For each block, a spatio-temporal context is defined based on nearest neighbors in the current and previous frames, and a prediction list is built. Then, the best matching vector within the list is chosen as an estimation of the block motion. Since coder and decoder are synchronous, only the index of the selected vector is needed at the decoder to reconstruct the motion field. To avoid the propagation of the error, an additional correction vector can be sent when prediction error exceeds a threshold. Furthermore, bitrate saving is achieved through an adaptive sorting of the prediction list of each block, which allows to reduce the entropy of the motion indexes. Tests demonstrate that the proposed method ensures a speed up over 1:200 as compared to full search, and a coding gain above 2, with a negligible loss of accuracy. This allows real-time implementation of VLBR software video coders on conventional PC platforms

Deep Learning for Mobile Multimedia: A Survey

Deep Learning (DL) has become a crucial technology for multimedia computing. It offers a powerful instrument to automatically produce high-level abstractions of complex multimedia data, which can be exploited in a number of applications, including object detection and recognition, speech-to- text, media retrieval, multimodal data analysis, and so on. The availability of affordable large-scale parallel processing architectures, and the sharing of effective open-source codes implementing the basic learning algorithms, caused a rapid diffusion of DL methodologies, bringing a number of new technologies and applications that outperform, in most cases, traditional machine learning technologies. In recent years, the possibility of implementing DL technologies on mobile devices has attracted significant attention. Thanks to this technology, portable devices may become smart objects capable of learning and acting. The path toward these exciting future scenarios, however, entangles a number of important research challenges. DL architectures and algorithms are hardly adapted to the storage and computation resources of a mobile device. Therefore, there is a need for new generations of mobile processors and chipsets, small footprint learning and inference algorithms, new models of collaborative and distributed processing, and a number of other fundamental building blocks. This survey reports the state of the art in this exciting research area, looking back to the evolution of neural networks, and arriving to the most recent results in terms of methodologies, technologies, and applications for mobile environments

Cost-Effective VoIP Services for Reducing Digital Divide in Developing Countries: Case of Study and Practical Implementation

Digital divide is one of the most relevant emergencies of developing countries. The situation is particularly difficult in central Africa, where wide zones are totally disconnected by any kind of communication services. In these areas (sometimes not very far from big towns) many people are living and critical infrastructures, like e.g. hospitals, cannot communicate with the external world. In this work, we are going to propose a feasible and low-cost solution for installing VoIP-based telephone services in peripheral areas not reached by fixed and cellular networking infrastructures. The proposed solution is based on a Wi-Fi radio bridge connecting a remote disconnected site with a gateway placed in a town. The gateway consists only of a PC connected to the PSTN by a suitable interfacing card. The gateway is equipped with the ASTERISK server, a software tool able at routing VoIP calls to a PSTN, as it would happen if a regular push-button telephone was employed. The system was developed and tested in open field and provided very good results in terms of efficiency and reliability. Such a positive testing phase encouraged developers to propose such kind of solution also for practical installation in interested countries. In particular, low hardware costs, easy of use and totally open-source software availability are regarded by potentially interested users as real strength points with respect to other available commercial solutions

A Multilevel Asymmetric Scheme for Digital Fingerprinting

The present paper proposes an asymmetric watermarking scheme suitable for fingerprinting and precision-critical applications. The method is based on linear algebra and is proved to be secure under projection attack. The problem of anonymous fingerprinting is also addressed, by allowing a client to get a watermarked image from a server without revealing her/his own identity. In particular, we consider the specific scenario where the client is a structured organization being trusted as a whole but involving possibly untrusted members. In such a context, where the watermarked copy can be made available to all members, but only authorized subgroups should be able to remove the watermark and recover a distortion-free image, a multilevel access to the embedding key is provided by applying Birkhoff polynomial interpolation. Extensive simulations demonstrate the robustness of the proposed method against standard image degradation operators

Glocal Multimedia Retrieval

Personal experience is intrinsically local while common knowledge is global. As a consequence, standard multimedia search engines suffer from a gap between local content and global concept, due to the diversity of context. Here we design a new multimedia retrieval system which integrates local diversity into an evolving global knowledge. A suitable personalization of global ontologies allows to extract information from multimedia content according to context-sensitive relevance, while a peer-to-peer communication model provides a decentralized and scalable logical architecture for media search

Situation-Aware Radio Resource Management for Multi-Rate MC-CDMA Wireless Networks Targeted at Multimedia Data Exchanges in Local Areas

In this report, a novel methodology for radio resource management is considered for multi-rate MC-CDMA WLAN indoor networking. The proposed strategy is based on resource reallocation and rate adaptation depending on the network situation, monitored in terms of achieved QoS by an intelligent access-point (AP). As they access the medium, all users send bit-rate requests on the basis of their application requirements. Then, the AP monitors the QoS in terms of frame-error-rate (FER) and decides a) to reallocate the radio resources (in terms of number of orthogonal subchannels) and b) to reduce the data rate, in order to improve throughput performances for those users penalized by heavy FER. The “rate downshift” process is continued until the FER measured by the AP allows data transfer with an acceptable QoS. The intelligent AP can also issue a “rate upshift” for users previously downshifted, or in presence of explicit requests, when the channel situation improves. Simulation results underline a general improvement of the aggregated throughput deriving from adaptively manage the QoS requirements with respect to the network situation, thus enabling good resource usage with the WLAN cell

Multi-Carrier Code Division Multiplexing of Multi-Layered MPEG4 Video Signals for Real-time Mobile Streaming Applications

In this report, a novel methodology for the efficient multiplexing and transmission of MPEG4- coded video signals over wireless networks will be presented and discussed. The proposed approach relies on the joint exploitation of variable-bit-rate (VBR) multicarrier code-division multiplexing (MC-CDM), together with MPEG4 coding with Fine-Grain-Scalability (FGS) in order to provide unequal error protection to the transmitted video stream.The innovative scheme proposed employs a shared bandwidth partitioned into orthogonal sub-channels in order to multiplex different layers of MPEG-4-coded signals. The highest number of sub-channels (and hence an increased frequency diversity) is assigned to the lowest-bit-rate base layer and the lowest number of sub-channels is assigned to the highest bit-rate enhancement layer. In such a way, base layer information contents are more protected against channel degradations than information contained in FGS enhancement layers, which can only yield a refinement of the quality of the decoded streams. A 2GHz LEO multicast satellite transmission to mobile users has been regarded as the application testbed for the proposed method. Results achieved in terms of PSNR point out that the VBR MC-CDM technique can provide better results than a conventional MPEG-4 single-layer MC-SS transmission.In the framework of a full-digital implementation of reconfigurable multimedia transceivers, the proposed VBR MC-CDM technique may be regarded as an interesting solution for reliable multimedia transmissions in mobile environments

On the Use of a Genetic-Based Approach for Antenna Array Control in a Scattering Environment

In this letter, an approach to adaptive antenna array optimisation based on genetic algorithms is assessed in a realistic scattering environment, where interfering signals are modelled using the Student's t-distribution. Simulation results demonstrate the effectiveness of the proposed array control strategy