With the rapid growth of video data traffic on the Internet and the development of new types of video transmission systems, the need for ad hoc video encoders has also increased. One such case involves Unmanned Aerial Vehicles (UAVs), widely known as drones, which are used in drone races, search and rescue efforts, capturing panoramic views, and so on. In this paper, we provide an efficiency comparison of the two most popular video encoders—H.264 and H.265—in a drone piloting system using first-person view (FPV). In this system, a drone is used to capture video, which is then transmitted to FPV goggles in real time. We examine the compression efficiency of 4K drone footage by varying parameters such as Group of Pictures (GOP) size, Quantization Parameter (QP), and target bitrate. The quality of the compressed footage is determined using four objective video quality measures: PSNR, SSIM, VMAF, and BRISQUE. Apart from video quality, encoding time and encoding energy consumption are also compared. The research was performed using numerous nodes on a supercomputer.

Despite the existing medical infrastructure being limited in terms of interoperability, the amount of medical multimedia transferred over the network and shared through various channels increases rapidly. In search of consultations with colleagues, medical professionals with the consent of their patients, usually exchange medical multimedia, mainly in the form of images, by using standard instant messaging services which utilize lossy compression algorithms. That consultation paradigm can easily lead to losses in image representation that can be misinterpreted and lead to the wrong diagnosis. This paper presents MIDOM—Medical Imaging and Diagnostics on the Move, a DICOM-based medical image communication system enhanced with a couple of variants of our previously developed custom lossless Classification and Blending Predictor Coder (CBPC) compression method. The system generally exploits the idea that end devices used by the general population and medical professionals alike are satisfactorily performant and energy-efficient, up to a point to support custom and complex compression methods successfully. The system has been implemented and appropriately integrated with Orthanc, a lightweight DICOM server, and a medical images storing PACS server. We benchmarked the system thoroughly with five real-world anonymized medical image sets in terms of compression ratios and latency reduction, aiming to simulate scenarios in which the availability of the medical services might be hardly reachable or in other ways limited. The results clearly show that our system enhanced with the compression methods in the question pays off in nearly every testing scenario by lowering the network latency to at least 60% of the latency required to send raw and uncompressed image sets and 25% in the best-case, while maintaining the perfect reconstruction of medical images and, thus, providing a more suitable environment for healthcare applications.

Summary form only given. In the paper we propose a new adaptive predictor which is based on the blending of multiple static predictors on the dynamically classified causal set of neighboring pixels. The predictor models the image properties around the current, unknown pixel and adjusts itself to the local image region. The main contribution of this work is the enhancement of the well known approach of predictor blends through highly adaptive determination of blending context on a pixel-by-pixel basis using classification technique. This allows modeling of more complex image structures such as nontrivially oriented edges and the periodicity and coarseness of textures.

In this paper, we present an overview of the European Processor Initiative (EPI), one of the cornerstones of the EuroHPC Joint Undertaking, a new European Union strategic entity focused on pooling the Union’s and national resources on HPC to acquire, build and deploy the most powerful supercomputers in the world within Europe. EPI started its activities in December 2018. The first three years drew processor and platform designers, embedded software, middleware, applications and usage experts from 10 EU countries together to co-design Europe’s first HPC Systems on Chip and accelerators with its unique Common Platform (CP) technology. One of EPI’s core activities also takes place in the automotive sector, providing architectural solutions for a novel embedded high-performance computing (eHPC) platform and ensuring the overall economic viability of the initiative.