In the newly finalized video coding standard, namely high efficiency video coding (HEVC), new notations like coding unit (CU), prediction unit (PU) and transformation unit (TU) are introduced to improve the coding performance. As a result, the reconstruction loop in intra encoding is heavily burdened to choose the best partitions or modes for them. In order to solve the bottleneck problems in cycle and hardware cost, this paper proposed a high-throughput and compact implementation for such a reconstruction loop. By “high-throughput”, it refers to that it has a fixed throughput of 32 pixel/cycle independent of the TU/PU size (except for 4×4 TUs). By “compact”, it refers to that it fully explores the reusability between discrete cosine transform (DCT) and inverse discrete cosine transform (IDCT) as well as that between quantization (Q) and de-quantization (IQ). Besides the contributions made in designing related hardware, this paper also provides a universal formula to analyze the cycle cost of the reconstruction loop and proposed a parallel-process scheme to further reduce the cycle cost. This design is verified on the Stratix IV FPGA. The basic structure achieved a maximum frequency of 150MHz and a hardware cost of 64K ALUTs, which could support the real time TU/PU partition decision for 4K×2K@20fps videos.

In the field of action recognition, Spatio-Temporal Interest Points (STIPs)-based features have shown high efficiency and robustness. However, most of state-of-the-art work to describe STIPs, they typically focus on 2-dimensions (2D) images, which ignore information in 3D spatio-temporal space. Besides, the compact representation of descriptors should be considered due to the costs of storage and computational time. In this paper, a novel local descriptor named 3D Gradient LBP is proposed, which extends the traditional descriptor Local Binary Patterns (LBP) into 3D spatio-temporal space. The proposed descriptor takes advantage of the neighbourhood information of cuboids in three dimensions, which accounts for its excellent descriptive power for the distribution of grey-level space. Experiments on three challenging datasets (KTH, Weizmann and UT Interaction) validate the effectiveness of our approach in the recognition of human actions.