The infinite disk : challenges from no limitations

Challenge: Managing and searching across multi-terabyte and potentially multi-petabyte personal stores of multimedia information

TRECVid 2011 Experiments at Dublin City University

This year the iAd-DCU team participated in three of the assigned TRECVid 2011 tasks; Semantic Indexing (SIN), Interactive Known-Item Search (KIS) and Multimedia Event Detection (MED). For the SIN task we presented three full runs using global features, local features and fusion of global, local features and relationships between concepts respectively. The evaluation results show that local features achieve better performance, with marginal gains found when introducing global features and relationships between concepts. With regard to our KIS submission, similar to our 2010 KIS experiments, we have implemented an iPad interface to a KIS video search tool. The aim of this year’s experimentation was to evaluate different display methodologies for KIS interaction. For this work, we integrate a clustering element for keyframes, which operates over MPEG-7 features using k-means clustering. In addition, we employ concept detection, not simply for search, but as a means of choosing most representative keyframes for ranked items. For our experiments we compare the baseline non-clustering system to a clustering system on a topic by topic basis. Finally, for the first time this year the iAd group at DCU has been involved in the MED Task. Two techniques are compared, employing low-level features directly and using concepts as intermediate representations. Evaluation results show promising initial results when performing event detection using concepts as intermediate representations

Spott : on-the-spot e-commerce for television using deep learning-based video analysis techniques

Spott is an innovative second screen mobile multimedia application which offers viewers relevant information on objects (e.g., clothing, furniture, food) they see and like on their television screens. The application enables interaction between TV audiences and brands, so producers and advertisers can offer potential consumers tailored promotions, e-shop items, and/or free samples. In line with the current views on innovation management, the technological excellence of the Spott application is coupled with iterative user involvement throughout the entire development process. This article discusses both of these aspects and how they impact each other. First, we focus on the technological building blocks that facilitate the (semi-) automatic interactive tagging process of objects in the video streams. The majority of these building blocks extensively make use of novel and state-of-the-art deep learning concepts and methodologies. We show how these deep learning based video analysis techniques facilitate video summarization, semantic keyframe clustering, and (similar) object retrieval. Secondly, we provide insights in user tests that have been performed to evaluate and optimize the application's user experience. The lessons learned from these open field tests have already been an essential input in the technology development and will further shape the future modifications to the Spott application

A Fpga-based Architecture For Led Backlight Driving

In recent years, Light-emitting Diodes (LEDs) have become a promising candidate for backlighting Liquid Crystal Displays [1] (LCDs). Compared with traditional Cold Cathode Fluorescent Lamps (CCFLs) technology, LEDs offer not only better visual quality, but also improved power efficiency. However, to fully utilized LEDs\u27 capability requires dynamic independent control of individual LEDs, which remains as a challenging topic. A FPGA-based hardware system for LED backlight control is proposed in this work. We successfully achieve dynamic adjustment of any individual LED\u27s intensity in each of the three color channels (Red, Green and Blue), in response to a real time incoming video stream. In computing LED intensity, four video content processing algorithms have been implemented and tested, including averaging, histogram equalization, LED zone pattern change detection and non-linear mapping. We also construct two versions of the system. The first employs an embedded processor which performs the above-mentioned algorithms on pre-processed video data; the second embodies the same functionality as the first on fixed hardware logic for better performance and power efficiency. The system servers as the backbone of a consolidated display, which yields better visual quality than common commercial displays, we build in collaboration with a group of researchers from CREOL at UCF

Deep Learning-Based Real-Time Quality Control of Standard Video Compression for Live Streaming

Ensuring high-quality video content for wireless users has become increasingly vital. Nevertheless, maintaining a consistent level of video quality faces challenges due to the fluctuating encoded bitrate, primarily caused by dynamic video content, especially in live streaming scenarios. Video compression is typically employed to eliminate unnecessary redundancies within and between video frames, thereby reducing the required bandwidth for video transmission. The encoded bitrate and the quality of the compressed video depend on encoder parameters, specifically, the quantization parameter (QP). Poor choices of encoder parameters can result in reduced bandwidth efficiency and high likelihood of non-conformance. Non-conformance refers to the violation of the peak signal-to-noise ratio (PSNR) constraint for an encoded video segment. To address these issues, a real-time deep learning-based H.264 controller is proposed. This controller dynamically estimates the optimal encoder parameters based on the content of a video chunk with minimal delay. The objective is to maintain video quality in terms of PSNR above a specified threshold while minimizing the average bitrate of the compressed video. Experimental results, conducted on both QCIF dataset and a diverse range of random videos from public datasets, validate the effectiveness of this approach. Notably, it achieves improvements of up to 2.5 times in average bandwidth usage compared to the state-of-the-art adaptive bitrate video streaming, with a negligible non-conformance probability below $10^{-2}$.Comment: arXiv admin note: text overlap with arXiv:2310.0685

The Internet and Prospective Engineers: Results Analysis for Studies Conducted During the Pandemic

The relevance of the study is justified by transition to distance learning that modifies the learning methods and principles during the pandemic to conform to remote training needs of students majoring in Land Management and Cadastres at Arctic State Agrotechnological University (ASAU). of the Republic of Sakha (Yakutia). The study objective was to substantiate the interaction between ASAU students and teachers in remote training organization within the pandemic period using network technologies. The study monitored the educational process dynamics during the pandemic. The study results evidence that during the pandemic, a particular priority in educational process arrangement at ASAU was given to enhancing the professional readiness of students and teachers and building up their competence in organizing their professional activities using the Internet in compliance with the latest requirements of the Federal State Educational Standards. The reference and experimental groups were sampled based on students’ interviews during the transition to remote access Internet-based educational process.  The practical implications of the study lie in identifying the distinctive features of teachers’ and students’ educational activities at ASAU during the pandemic. These results can be adapted and implemented in the system of prospective engineers’ training in other regional universities in the north-east of Russia

The Internet and Prospective Engineers: Results Analysis for Studies Conducted During the Pandemic

The relevance of the study is justified by transition to distance learning that modifies the learning methods and principles during the pandemic to conform to remote training needs of students majoring in Land Management and Cadastres at Arctic State Agrotechnological University (ASAU). of the Republic of Sakha (Yakutia). The study objective was to substantiate the interaction between ASAU students and teachers in remote training organization within the pandemic period using network technologies. The study monitored the educational process dynamics during the pandemic. The study results evidence that during the pandemic, a particular priority in educational process arrangement at ASAU was given to enhancing the professional readiness of students and teachers and building up their competence in organizing their professional activities using the Internet in compliance with the latest requirements of the Federal State Educational Standards. The reference and experimental groups were sampled based on students’ interviews during the transition to remote access Internet-based educational process.  The practical implications of the study lie in identifying the distinctive features of teachers’ and students’ educational activities at ASAU during the pandemic. These results can be adapted and implemented in the system of prospective engineers’ training in other regional universities in the north-east of Russia