Fast adaptive motion estimation for H.264

H.264 motion estimation achieves better compression efficiency of video coding than previous video standards (e.g. MPEG-2, H.263, and JPEG). But it leads to higher computational cost and complexity in coding. In this study we propose an efficient early termination searching method to reduce the computational complexity and achieve better compression ratio. Adaptive search strategy is applied to reduce the search point in a search range. Furthermore this study presents an analysis of the performance of the proposed algorithm in terms of motion estimation time, total encoding time, and video quality (PSNR). Simulation result shows that compared to Full Search (FS), this algorithm achieves up to 60% reduction in motion estimation time without degrading the video quality

Fast adaptive motion estimation search algorithm for H.264 encoder

The latest H.264/AVC encoder adopted more advanced techniques such as multiple reference-frame motion estimation, 4 x 4 integers Discrete Cosine Transform (DCT), intra prediction, de-blocking filter, quarter pixel Motion Estimation (ME) with variable block size and novel entropy. Motion estimation is a technique of video compression and video processing applications; it extracts motion information from the video sequence. Multiple reference-frame motion estimation can gain better compression efficiency of video coding for H.264 than previous video standards (e.g MPEG-2,H.263, JPEG). But it leads to higher computational cost and complexity in coding. In this study we proposed an efficient early termination searching method to reduce the computational complexity and achieve better compression ratio. Adaptive search strategy is applied to reduce the search point in a search range. Furthermore this study presents an analysis of the performance of the proposed algorithm in terms of motion estimation time, total encoding time, video quality (PSNR), and bit rate. Simulation result shows that as compared to previous research, this algorithm achieves up to average 60% reduction in motion estimation time without degrading the video quality

Towards secure fog computing: A survey on trust management, privacy, authentication, threats and access control

Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern that should come into consideration. Therefore, to provide the necessary security for Fog devices, there is a need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works, need to be identified and aggregated. On the other hand, privacy preservation for user’s data in Fog devices and application data processed in Fog devices is another concern. To provide the appropriate level of trust and privacy, there is a need to focus on authentication, threats and access control mechanisms as well as privacy protection techniques in Fog computing. In this paper, a survey along with a taxonomy is proposed, which presents an overview of existing security concerns in the context of the Fog computing paradigm. Moreover, the Blockchain-based solutions towards a secure Fog computing environment is presented and various research challenges and directions for future research are discussed