In recent years, remarkable performance improvements have been produced by deep convolutional neural networks (CNN) for single image super-resolution (SISR). Nevertheless, a high proportion of CNN-based SISR models are with quite a few network parameters and high computational complexity for deep or wide architectures. How to more fully utilize deep features to make a balance between model complexity and reconstruction performance is one of the main challenges in this field. To address this problem, on the basis of the well-known information multi-distillation model, a multi-attention feature distillation network termed as MAFDN is developed for lightweight and accurate SISR. Specifically, an effective multi-attention feature distillation block (MAFDB) is designed and used as the basic feature extraction unit in MAFDN. With the help of multi-attention layers including pixel attention, spatial attention, and channel attention, MAFDB uses multiple information distillation branches to learn more discriminative and representative features. Furthermore, MAFDB introduces the depthwise over-parameterized convolutional layer (DO-Conv)-based residual block (OPCRB) to enhance its ability without incurring any parameter and computation increase in the inference stage. The results on commonly used datasets demonstrate that our MAFDN outperforms existing representative lightweight SISR models when taking both reconstruction performance and model complexity into consideration. For example, for × 4 SR on Set5, MAFDN (597K/33.79G) obtains 0.21 dB/0.0037 and 0.10 dB/0.0015 PSNR/SSIM gains over the attention-based SR model AFAN (692K/50.90G) and the feature distillation-based SR model DDistill-SR (675K/32.83G), respectively.

High Efficiency Video Coding (HEVC) achieves significant coding performance over H.264. However, the performance gain is achieved at the cost of substantially higher encoding complexity, in which the coding tree unit (CTU) partition is one of the most time-consuming parts due to the rate-distortion optimization-based ergodic search of all possible quad-tree partitions. To address this problem, this paper proposes a texture-classification accelerated convolutional neural network (CNN)-based fast intra CU partition scheme to reduce the encoding complexity for intra-coding in HEVC, by taking into consideration of the heterogeneous texture characteristics into the CNN-based classification. First, a threshold-based texture classification model is developed to identify the heterogeneous and homogeneous CTUs, through jointly consideration of the CU depth, quantization parameter and texture complexity. Second, three different CNN structures are designed and trained to predict the CU partition mode for each CU layer in the heterogeneous CTUs. Finally, extensive experimental results show that the proposed scheme can reduce intra-mode encoding time by 62.13% with negligible BD-rate loss of 2.01%, consistently outperforming two state-of-the-art CNN-based schemes in terms of both coding performance and complexity reduction.

As the next generation standard of video coding, the High Efficiency Video Coding (HEVC) achieves significantly better coding efficiency than all existing video coding standards. A Coding Unit (CU) quad tree concept is introduced to HEVC to improve the coding efficiency. Each CU node in quad tree will be traversed by depth first search process to find the best Coding Tree Unit (CTU) partition. Although this quad tree search process can obtain the best CTU partition, it is very time consuming, especially in interframe coding. To alleviate the encoder computation load in interframe coding, a fast CU depth decision method is proposed by reducing the depth search range. Based on the depth information correlation between spatio-temporal adjacent CTUs and the current CTU, some depths can be adaptively excluded from the depth search process in advance. Experimental results show that the proposed scheme provides almost 30% encoder time savings on average compared to the default encoding scheme in HM8.0 with only 0.38% bit rate increment in coding performance.