RESUMO
Block partition structure is a critical module in video coding scheme to achieve significant gap of compression performance. Under the exploration of the future video coding standard, named Versatile Video Coding (VVC), a new Quad Tree Binary Tree (QTBT) block partition structure has been introduced. In addition to the QT block partitioning defined in High Efficiency Video Coding (HEVC) standard, new horizontal and vertical BT partitions are enabled, which drastically increases the encoding time compared to HEVC. In this paper, we propose a lightweight and tunable QTBT partitioning scheme based on a Machine Learning (ML) approach. The proposed solution uses Random Forest classifiers to determine for each coding block the most probable partition modes. To minimize the encoding loss induced by misclassification, risk intervals for classifier decisions are introduced in the proposed solution. By varying the size of risk intervals, tunable trade-off between encoding complexity reduction and coding loss is achieved. The proposed solution implemented in the JEM-7.0 software offers encoding complexity reductions ranging from 30average for only 0.7% to 3.0% Bjxntegaard Delta Rate (BDBR) increase in Random Access (RA) coding configuration, with very slight overhead induced by Random Forest. The proposed solution based on Random Forest classifiers is also efficient to reduce the complexity of the Multi-Type Tree (MTT) partitioning scheme under the VTM-5.0 software, with complexity reductions ranging from 25% to 61% in average for only 0.4% to 2.2% BD-BR increase.
RESUMO
Transmission and compression technologies advancement over the past decade led to a shift of multimedia content towards cloud systems. Multiple copies of the same video are available through numerous distribution systems. Different compression levels, algorithms and resolutions are used to match the requirements of particular applications. As 4k display technologies are rapidly adopted, resolution enhancement algorithms are of vital importance. Current solutions do not take into account the particularities of different video encoders, while video reconstruction methods from compressed sources do not provide resolution enhancement. In this paper, we propose a multi source compressed video enhancement framework, where each description can have a different compression level and resolution. Using a variational formulation based on a modern proximal dual splitting algorithm, we efficiently combine multiple descriptions of the same video. Two applications are proposed: combining two compressed low resolution (LR) descriptions of a video sequence into a high resolution (HR) description and enhancing a compressed HR video using a LR compressed description. Tests are performed over multiple video sequences encoded with high efficiency video coding, at different compression levels and resolutions obtained through multiple down-sampling methods.
RESUMO
The management of medical emergency, in particular cardiac emergency, requests prompt intervention and the possibility to communicate in real time from the emergency area / ambulance to the hospital as much diagnostic information as possible about the patient. This would enable a prompt emergency diagnosis and operation and the possibility to prepare the appropriate actions in the suitable hospital department. To address this scenario, the CONCERTO European project proposed a wireless communication system based on a novel cross-layer architecture, including the integration of building blocks for medical media content fusion, delivery and access. This paper describes the proposed system architecture, outlining the developed components and mechanisms, and the evaluation of the proposed system, carried out in a hospital with the support of medical staff. The technical results and the feedback received highlight the impact of the CONCERTO approach in the healthcare domain, in particular in enabling a prompt and reliable diagnosis in challenging medical emergency scenarios.