High resolution (HR) videos are easy to go beyond the band-width limit during transmission after encoding. Downsampling followed by upsampling is a well-known strategy for compressing HR videos with limited bandwidth. Versatile video coding (VVC) provides a reference picture resampling (RPR) functionality to consider the limited bandwidth. In this paper, we propose a convolutional neural network (CNN) filter for super-resolution (SR) with the RPR functionality in VVC. We design a lightweight SR network that combines CNN with the RPR functionality in VVC, called lightweight network of multi-level mixed scale and depth information with attention mechanism (LMSDANet). For LMSDANet, we provide a lightweight block of multi-mixed scale and depth information with attention (LMSDAB) to extract multi-scale and convolutional layer depth information while enhancing the representation of features. Compared with VTM-11.0_NNVC-2.0 anchor, LMSDANet achieves {-9.16% (Y), 17.03% (U), -7.61% (V)} and {-4.14% (Y), 6.34% (U), -2.25% (V)} BD-rate changes (average on A1 and A2) in AI and RA configurations, respectively.

In this paper, we propose a convolutional neural network (CNN) filter for reference picture resampling (RPR)-based super-resolution (SR) with wavelet decomposition. The proposed CNN filter takes the low resolution (LR) reconstructed frame (RecLR), LR prediction frame (PreLR) and high resolution (HR) RPR upsampled frame (RPRoutput) as the input for RPR-based SR. Thus, the proposed CNN filter not only learns a mapping function between LR and HR images, but also effectively removes blocking artifacts in the reconstructed frame. We adopt wavelet decomposition to make RPRoutputthe same size as RecLRand PreLRas well as obtain the relationship between high frequency (HF) and low frequency (LF) components. To maximize feature reuse under the limited parameters, we design a residual spatial and channel attention block (RSCB) that combines residual blocks with spatial attention and channel attention to learn the weighted local information and global information in different receptive fields. Experimental results show that the proposed CNN filter achieves -8.98% and -4.05% BD-rate reductions on Y channel in AI and RA configurations over VTM-11.0_NNVC-2.0 anchor, respectively.

In versatile video coding (VVC), the in-loop filters suppress compression artifacts while reducing distortion. However, they have a limit of removing complicated compression artifacts due to the hand-crafted design. In this paper, we propose a convolutional neural network (CNN)-based in-loop filter for VVC intra coding. We introduce depthwise separable convolution and attention mechanism to make the proposed network lightweight and efficient. Moreover, we present two basic modules of residual attention block (RAB) and weakly connected attention block (WCAB) to extract and refine features. Besides, we design a multi-stage training strategy based on progressive learning to maximize the learning ability of the proposed network. Compared with VTM-11.0_NNVC-2.0 anchor, the proposed in-loop filter achieves average -7.08% (Y), -12.46% (U), -12.75% (V) BD-rate gains under All Intra (AI) configuration.

In this paper, we propose a convolutional neural network (CNN)-based post-processing filter for video compression with multi-scale feature representation. The discrete wavelet transform (DWT) decomposes an image into multi-frequency and multi-directional sub-bands, and can figure out artifacts caused by video compression with multi-scale feature representation. Thus, we combine DWT with CNN and construct two sub-networks: Step-like sub-band network (SLSB) and mixed enhancement network (ME). SLSB takes the wavelet subbands as input, and feeds them into the Res2Net group (R2NG) from high frequency to low frequency. R2NG consists of Res2Net modules and adopts spatial and channel attentions to adaptively enhance features. We combine the high frequency sub-band output with the low frequency sub-band in R2NG to capture multi-scale features. ME uses mixed convolution composed of dilated convolution and standard convolution as the basic block to expand the receptive field without blind spots in dilated convolution and further improve the reconstruction quality. Experimental results demonstrate that the proposed CNN filter achieves average 2.13%, 2.63%, 2.99%, 4.8%, 3.72% and 4.5% BD-rate reductions over VTM 11.0-NNVC anchor for Y channel on A1, A2, B, C, D and E classes of the common test conditions (CTC) in AI, RA and LDP configurations, respectively.