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This paper proposes a fast mode decision algorithm for 3D High Efficiency Video Coding (3D-HEVC) depth intra coding. In the current 3D-HEVC design, it is observed that for most of the cases, full Rate-Distortion (RD) cost search of Bi-partition mode could be skipped since most coding units (CUs) of depth map are very flat or smooth while Bipartition modes are designed for CUs with edge or sharp transition. Using the rough RD cost value calculated by HEVC Rough Mode Decision as a selection threshold, we propose a fast Bi-partition modes selection algorithm to speed up the encoding process. The test result for the proposed fast algorithm reports 34.4% encoding time saving with 0.3% bitrate increasing on synthesized view for AllIntra test case. Moreover, by simply varying the selection threshold, we can make a tradeoff between encoding time saving and bitrate loss based on the requirement of different applications.

The combination of features from the convolutional layer and the fully connected layer of a convolutional neural network (CNN) provides an effective way to improve the performance of crime scene investigation (CSI) image classification. However, in existing work, as the weights in feature fusion do not change after the training phase, it may produce inaccurate image features which affect classification results. To solve this problem, this paper proposes an adaptive feature fusion method based on an auto-encoder to improve classification accuracy. The method includes the following steps: Firstly, the CNN model is trained by transfer learning. Next, the features of the convolution layer and the fully connected layer are extracted respectively. These extracted features are then passed into the autoencoder for further learning with Softmax normalisation to obtain the adaptive weights for performing final classification. Experiments demonstrated that the proposed method achieves higher CSI image classification performance compared with fix weights feature fusion.

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