The new generation video standard, i.e., High-efficiency Video Coding (HEVC), shows a significantly improved efficiency relative to the last standard, i.e., H.264. However, the quad tree structured coding units (CUs), which are adopted in HEVC to improve compression efficiency, cause high computational complexity. In this study, a novel fast algorithm is proposed for CU partition in intra coding to reduce the computational complexity. A rough minimum depth prediction of the largest CU method and an early termination method for CU partition based on the total coding bits of the current CU are employed. Many approaches have been proposed to reduce the encoding complexity of HEVC, but these methods do not use the total coding bits of the current CU as the main basis for judgment to judge the CU complexity. Compared with the reference software HM16.6, the proposed algorithm reduces encoding time by 45% on average and achieves an approximately 1.1% increase in Bjntegaard delta bit rate and a negligible peak signal-to-noise ratio loss.

The fast evolving credential attacks have been a great security challenge to current password-based information systems. Recently, biometrics factors like facial, iris, or fingerprint that are difficult to forge rise as key elements for designing passwordless authentication. However, capturing and analyzing such factors usually require special devices, hindering their feasibility and practicality. To this end, we present WiASK, a device-free WiFi sensing enabled Authentication System exploring Keystroke dynamics. More specifically, WiASK captures keystrokes of a user typing a pre-defined easy-to-remember string leveraging the existing WiFi infrastructure. But instead of focusing on the string itself which are vulnerable to password attacks, WiASK interprets the way it is typed, i.e., keystroke dynamics, into user identity, based on the biologically validated correlation between them. We prototype WiASK on the low-cost off-the-shelf WiFi devices and verify its performance in three real environments. Empirical results show that WiASK achieves on average 93.7% authentication accuracy, 2.5% false accept rate, and 5.1% false reject rate.