We demonstrate a reflectivity and depth imaging Lidar system based on a novel photon arrival time measurement method. In this method, the arrival time of photons in a scanning position is continuously measured with a common starting point. The number of laser pulses is counted by a specially designed field programmable gate array (FPGA) control module as the coarse time of arrival photon. Time interval between arrival photon and the nearest coming laser pulse is measured by a time-correlated single-photon counting (TCSPC) module as the fine time of arrival photon. Using the system, not only the singlephoton counting imaging can be realized, but also the first photon imaging, the first two photons imaging, etc. can be realized. A photon statistical model based on the doubly stochastic Poisson point processes, a time-gated filtering algorithm, and the reflectivity algorithm based on maximum likelihood estimation are derived. High-sensitivity reflectivity and depth imaging with a resolution of 512 × 512 pixels are achieved. The experimental results show that the horizontal spatial resolution is 2 mm, the vertical depth resolution is 5.375 cm, and the average number of photons per pixel is less than 1.3 photons.

H.264 has adoptedUMHexagonS algorithm as fast motion estimation algorithm for integer pixel formally, but this algorithm has some shortages such as searchpointsare lots, quantity of operation is kind of big, costs much time, and so on. These need to be solved as soon as possible. This paper introduces and analyses UMHexagonS algorithm. In order to solve these problems, this paper brings forwardclassification strategy for search algorithm according to statistical properties of the motion vector prediction value, and improvesthe template basedon original algorithm according to the characteristic of center bias of motion vector. The results of experimentsshow that the improved algorithm in this paper reduces the time of motion estimation by 15%~27%. At the meantime, it can ensure PSNR and rate basically unchanged.

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