SSIM Prediction for H.265HEVC based on Convolutional Neural Networks In signal compression, distortion information is significant for rate distortion optimization. In this paper, we propose a convolutional neural network (CNN) to predict distortion information for H.265HEVC. With the strong representation power of CNN, structural similarity (SSIM) maps indicating distortion information can be predicted directly in an endtoend, pixeltopixel way. Different from traditional CNNs which focus on learning onetoone mappings from input to output, we show that our CNN model can predict SSIM maps conditioned on quantization parameters (QPs), realizing onetomany mappings. To construct our CNN network, QP labels are designed as conditions to feed the CNN model. We also apply symmetrical network architecture and multilevel feature fusion method to ensure our network can utilize both highlevel semantic features and lowlevel structure features. The experiments on MS COCO database demonstrate the effectiveness of our CNNbased method for SSIM prediction.

CNN Accelerated Intra Video Coding, Where Is the Upper Bound? The very high complexity of the High Efficiency Video Coding standard (HEVC) is the main hurdle for its wide deployment and use. To tackle this problem, a number of recent research outcomes exploit Convolutional Neural Network (CNN) in each HEVC module for reducing the coding complexity. In this paper an effective method to analyse the potential of CNN techniques to reduce the computational cost of HEVC is proposed. A theoretical upper bound for the effectiveness of this approach in common HEVC modules is investigated. The theoretical maximum of learningbased complexity reduction in HEVC and possible reasons for RateDistortion (RD) loss are investigated. On the basis of this analysis, an Intra Video Coding Acceleration (IVCA) scheme is proposed, where Border Considered CNN (BCCNN) based Coding Unit (CU) partition and heuristic Prediction Unit (PU) partition are seamlessly integrated. According to the experimental results, 66.7% of intra coding time can be saved with negligible 1.71% Bjontegaard delta bitrate (BDBR) loss. These results partially demonstrate the superiority of the proposed technique against other stateoftheart approaches aiming at reducing HEVC complexity in intra mode.