High-efficiency video coding (HEVC) is a new video coding standard being developed by the Joint Collaborative Team on Video Coding. HEVC adopted numerous new tools, such as more flexible data structure representations, which include the coding unit (CU), prediction unit, and transform unit. In the partitioning of the largest coding unit (LCU) into CUs, rate distortion optimization (RDO) is applied. However, the computation complexity of RDO is too high for real-time application scenarios. Based on studies on the relationship between CUs and their entropy, this paper proposes a fast algorithm based on entropy to partition LCU as a substitute for RDO in HEVC. Experimental results show that the proposed entropy-based LCU partition algorithm can reduce coding time by 62.3% on average, with an acceptable loss of 3.82% using Bjøntegaard delta rate.

High Efficiency Video Coding (HEVC or H.265), the latest international video coding standard, displays a 50% bit rate reduction with nearly equal quality and dramatically higher coding complexity compared with H.264. Unlike other fast algorithms, we first propose an algorithm that combines the CU coding bits with the reduction of unnecessary intra-prediction modes to decrease computational complexity. In this study, we first analyzed the statistical relationship between the best mode and the costs calculated through Rough Mode Decision (RMD) process and proposed an effective mode decision algorithm in intra-mode prediction process. We alleviated the computation difficulty by carrying out the RMD process in two stages, reducing 35 modes down to 11 modes in the first RMD process stage, and adding modes adjacent to the most promising modes selected during the first stage into the second RMD stage. After these two stages, we had two or three modes ready to be used in the rate distortion operation (RDO) process instead of the three or eight in the original HEVC process, which significantly reduced the number of unnecessary candidate modes in the RDO process. We then used the coding bits of the current coding unit (CU) as the main basis for judging its complexity and proposed an early termination method for CU partition based on the number of coding bits of the current CU. Experimental results show that the proposed fast algorithm provides an average time reduction rate of 53% compared to the reference HM-16.12, with only 1.7% Bjontegaard delta rate increase, which is acceptable for Rate-Distortion performance.

For Wireless Sensor Networks, energy efficiency is always a key consideration in system design. Compressed sensing is a new theory which has promising prospects in WSNs. However, how to construct a sparse projection matrix is a problem. In this paper, based on a Bayesian compressed sensing framework, a new adaptive algorithm which can integrate routing and data collection is proposed. By introducing new target node selection metrics, embedding the routing structure and maximizing the differential entropy for each collection round, an adaptive projection vector is constructed. Simulations show that compared to reference algorithms, the proposed algorithm can decrease computation complexity and improve energy efficiency.