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Versatile video coding (VVC/H.266) is the newest video compression standard, which is developed by the Joint Video Experts Team. Compared with previous encoding schemes, VVC achieves higher compression efficiency by introducing a new partition structure and additional intra prediction modes but results in high computational complexity. To efficiently solve this problem of redundant processing in quad-tree with nested multi-type tree structures and intra mode prediction, we propose a texture analysis-based ternary tree (TT) and binary tree (BT) partition strategy, and a gradient-based intra mode decision method to accelerate TT and BT partition and intra mode prediction, separately. The texture complexity and prediction direction of coding unit (CU) is calculated by texture detection method. A texture analysis-based TT and BT partition strategy is established by using the regression method based on analyzing the texture complexity of the CU. Then, a texture analysis-based TT and BT partition strategy is applied to reduce the redundant partition for each CU. By using the prediction direction of CU, a gradientbased intra mode decision method is established for skipping the impossible modes for each CU. Experimental results revealed that the proposed method could save 49.49% in encoding time and increase the Bjontegaard delta bit rate (BDBR) by only 0.56%. It confirms that the proposed method achieved high efficiency and a good balance between the BDBR and time saving.INDEX TERMS H.266/VVC, fast CU partition, intra mode decision, gradient-based, regression

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Visual sensor networks (VSNs) have numerous applications in fields such as wildlife observation, object recognition, and smart homes. However, visual sensors generate vastly more data than scalar sensors. Storing and transmitting these data is challenging. High-efficiency video coding (HEVC/H.265) is a widely used video compression standard. Compare to H.264/AVC, HEVC reduces approximately 50% of the bit rate at the same video quality, which can compress the visual data with a high compression ratio but results in high computational complexity. In this study, we propose a hardware-friendly and high-efficiency H.265/HEVC accelerating algorithm to overcome this complexity for visual sensor networks. The proposed method leverages texture direction and complexity to skip redundant processing in CU partition and accelerate intra prediction for intra-frame encoding. Experimental results revealed that the proposed method could reduce encoding time by 45.33% and increase the Bjontegaard delta bit rate (BDBR) by only 1.07% as compared to HM16.22 under all-intra configuration. Moreover, the proposed method reduced the encoding time for six visual sensor video sequences by 53.72%. These results confirm that the proposed method achieves high efficiency and a favorable balance between the BDBR and encoding time reduction.