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.

Unmanned Aerial Vehicles (UAVs) are increasingly being used in a variety of applications, including entertainment, surveillance, and delivery. However, the real-time Motion Estimation (ME) of UAVs is challenging due to the high speed and unpredictable movements of these vehicles. This paper presents a novel algorithm for optimizing ME for Remotely Controlled Vehicles (RCVs), with a particular focus on UAVs. The proposed algorithm, called Motion Dynamics Input Search (MDIS), incorporates information from vehicle motion dynamics estimation to enhance the accuracy and efficiency of ME. The MDIS algorithm addresses the challenges associated with real-time ME in RCVs by leveraging user input to guide the search for the most similar blocks in the previous video frame. Through extensive experimentation and evaluation, this study demonstrates the effectiveness of the proposed algorithm in improving ME performance for RCVs. The findings highlight the potential impact of user interaction and motion dynamics estimation in shaping the future of ME algorithms for RCVs and similar applications.

Streaming has emerged as an important model in present—day applications, ranging from multimedia to scientific computing. Moreover, the emergence of new multicore architectures has resulted with new challenges in efficient utilization of available computational resources. Streaming model offers the portability and scalability of performance with the increasing number of cores. In this paper we propose a tool which enables the implementation of the compute—intensive stream processing kernels as portable modules in general—purpose applications. Resulting modules can be efficiently reused with high degree of scalability in regard to increasing number of processing cores.

In the pursuit of effective real-time video transmission for First-Person View (FPV) drone systems, optimizing the encoding process is paramount. Traditional encoding methods, reliant on pre-encoding demosaicking, often fall short in balancing the trade-off between video quality and latency, essential for seamless real-time feedback. This work proposes a novel approach by deferring the demosaicking process to the decoder side, thereby encoding the rearranged Bayer pattern (RGGB) data directly. This deferment significantly reduces the input data size, to the tune of a threefold reduction, thereby achieving a more expeditious encoding process. The tailored encoder and decoder architecture ensures the accurate reconstruction of the full-color image on the decoder side. Through a comprehensive evaluation, leveraging a specialized video quality assessment framework designed for FPV drone footage, our findings illuminate the substantial benefits of our proposed method. Specifically, it achieves faster encoding times and reduced computational overhead, pivotal for low-latency applications. Furthermore, this study opens avenues for integrating advanced encoding techniques into commercial FPV drone systems, potentially enriching user experiences across various applications. Our research not only addresses a critical gap in real-time video transmission but also sets the stage for future exploration into optimizing encoding methodologies for the next generation of FPV drone technologies.