3D-HEVC (The 3D Extension of High Efficiency Video Coding) is the newest 3D video coding standard, which enriches multimedia applications with the video format of multi-view plus depth. For the depth map coding in 3D-HEVC, the advanced coding tools enhance the coding efficiency of the depth map and the quality of the synthesized view. However, the time consumption and complexity of 3D-HEVC also increase significantly. This paper utilizes the characteristics of human visual system to propose a fast algorithm based on visual perception for the acceleration of the depth intra coding of 3D-HEVC. The depth map is segmented into different regions by Otsu's auto-thresholding. The dominate edge direction is categorized for each prediction unit. We detect the perceptual edge based on just noticeable depth difference model to extract the area that may affect the visual perception. According to depth map segmentation and edge distribution, we reduce the corresponding intra angular modes and determine whether to perform depth modelling mode. We also incorporate the boundary continuity and rate-distortion cost thresholding to propose the fast coding unit decision. The experimental results show that the proposed algorithm eliminates 53.09% of the depth coding time with only 0.15% BD-BR on average. The coding performance of the proposed algorithm outperforms the previous works significantly.

Underwater image processing has been shown to exhibit significant potential for exploring underwater environments. It has been applied to a wide variety of fields, such as underwater terrain scanning and autonomous underwater vehicles (AUVs)-driven applications, such as image-based underwater object detection. However, underwater images often suffer from degeneration due to attenuation, color distortion, and noise from artificial lighting sources as well as the effects of possibly low-end optical imaging devices. Thus, object detection performance would be degraded accordingly. To tackle this problem, in this article, a lightweight deep underwater object detection network is proposed. The key is to present a deep model for jointly learning color conversion and object detection for underwater images. The image color conversion module aims at transforming color images to the corresponding grayscale images to solve the problem of underwater color absorption to enhance the object detection performance with lower computational complexity. The presented experimental results with our implementation on the Raspberry pi platform have justified the effectiveness of the proposed lightweight jointly learning model for underwater object detection compared with the state-of-the-art approaches.