As the upcoming video coding standard, Versatile Video Coding (i.e., VVC) achieves up to 30% Bjøntegaard delta bit-rate (BD-rate) reduction compared with High Efficiency Video Coding (H.265/HEVC). To eliminate or alleviate different kinds of compression artifacts like blocking, ringing, blurring and contouring effects, three in-loop filters, i.e. de-blocking filter (DBF), sample adaptive offset (SAO) and adaptive loop filter (ALF), have been involved in VVC. Recently, Convolutional Neural Network (CNN) has attracted tremendous attention and shows great potential in many tasks in image processing. In this work, we design a CNN-based in-loop filter as an integrated single-model solution which is adaptive to almost any scenarios in video coding. An architecture named as ADCNN (i.e., Attention based Dual-scale CNN) with an attention based processing block is proposed to reduce artifacts of I frames and B frames, which take advantage of informative priors such as the quantization parameter (QP) and partitioning information. Different from existing CNN-based filtering methods, which are mainly designed for the luma component and may need to train different models for different QPs, the proposed filter is adapted to different QPs and different frame types, and all the components (i.e., both luma and chroma) are processed simultaneously with feature exchange and fusion between components for information supplementary. Experimental results show that the proposed ADCNN filter can achieve 6.54%, 13.27%, 15.72% BD-rate savings for Y, U, V respectively under the all intra configuration and 2.81%, 7.86%, 8.60% BD-rate savings under the random access configuration. It can be used to replace all the conventional in-loop filters and also outperforms them without increase in encoding time. INDEX TERMSCoding artifacts, convolutional neural network (CNN), in-loop filter, video coding, versatile video coding (VVC).

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Modern Aero-engine increasingly requires higher propulsive efficiency, lower weight and less fuel consumption, which motivates researchers all over the world to explore novel technology to achieve these goals. Blockerless cascade thrust reverser is one of such innovative technology, using high pressure secondary flow injecting into fan bypass to fulfill reverse thrust instead of traditional blocker door. The efficiency of thrust reverser is a critical index of the technology, which is a function of aerodynamic and geometric parameters, including fan pressure ratio, secondary injection pressure ratio, injection angle, and injection position. The present study aims to investigate the effects of these parameters on reverse performance through basic demonstration experiments. Firstly, a simple experimental model was designed, which would be conducted on cascade experimental facility. Then, experiments were performed at static conditions, with fixed fluidic injection slot, variable injection angles (30°, 45°, 60°, 90°) at positions of Xj = 38.77mm, 58.77mm, 78.77mm, 98.77mm, 118.77mm, with the scheduled fan pressure ratios (FPR from 1.3 to 1.7) and secondary to ambient pressure ratios (SPR from 2.0 to 6.0). The results indicate that there exits an optimum injection location and injection angle for maximum thrust efficiency, and performance of blockerless cascade thrust reverser varies with diverse fan pressure ratio and secondary injection pressure ratio.