SSIM Prediction for H.265/HEVC based on Convolutional Neural Networks
2019 IEEE Visual Communications and Image Processing (VCIP)(2019)
摘要
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.265/HEVC. With the strong representation power of CNN, structural similarity (SSIM) maps indicating distortion information can be predicted directly in an end-to-end, pixel-to-pixel way. Different from traditional CNNs which focus on learning one-to-one mappings from input to output, we show that our CNN model can predict SSIM maps conditioned on quantization parameters (QPs), realizing one-to-many mappings. To construct our CNN network, QP labels are designed as conditions to feed the CNN model. We also apply symmetrical network architecture and multi-level feature fusion method to ensure our network can utilize both high-level semantic features and low-level structure features. The experiments on MS COCO database demonstrate the effectiveness of our CNN-based method for SSIM prediction.
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关键词
SSIM,distortion prediction,convolutional neural network,H.265/HEVC,feature fusion,QP label
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