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Immersive media representation format based on point clouds has underpinned significant opportunities for extended reality applications. Point cloud in its uncompressed format require very high data rate for storage and transmission. One approach to compress point clouds is the video based point cloud compression (V-PCC) technique which projects a dynamic point cloud into geometry and texture video sequences. The projected texture video is then coded using the coding tools offered by modern video coding standard like HEVC.

Flow-based generative models are successfully applied in image generation tasks, where an invertible neural network (INN) is built up based on flow steps. Learning-based compression commonly transforms the input into a compact space and then implements a reconstruction network in the decoder accordingly. By utilizing low-resolution images, traditional or adaptive downsamplers with their corresponding traditional or learned upsamplers usually achieve better coding quality at a low bit-rate.

Existing compression artifacts reduction methods aim to restore images on pixel-level, which can improves human visual experience. However, in many applications, large-scale images are collected not for visual examination by human. Instead, they are used for many high-level vision tasks usually by Deep Neural Networks (DNN). One fundamental problem here is whether existing artifacts reduction methods can help DNNs improve the performance of the high-level tasks. In this paper, we find that these methods have limited performance improvements to high-level tasks, even bring negative effects.

In video coding, block partition segments image into non-overlap blocks for individual coding, the structure of which is becoming more and more flexible along with the development of video coding standards. Multiple types of tree structures have been proposed recently, which extensively improved the complexity of the encoding process due to recursive rate-distortion search for the optimal partition. In this paper, a two-stage Convolutional Neural Network (CNN) based partition structure prediction method is proposed to bypass the decision process of the block size in intra frame coding.

In the Versatile Video Coding~(VVC) standard, adaptive loop filter~(ALF), including Geometry transformation-based Adaptive Loop Filter~(GALF) and Cross Component Adaptive Loop Filter~(CCALF), plays an essential role in reducing compression artifacts. However, it also has high coding complexity and requires many picture buffer accesses in the encoder that will increase external memory access and is unfriendly to the software and hardware design.

In recent years, using compressed sensing (CS) as a cryptosystem has drawn more and more attention since this cryptosystem can perform compression and encryption simultaneously. However, this cryptosystem is vulnerable to known-plaintext attack (KPA) under multi-time-sampling (MTS) scenario due to the linearity of its encoding process.

As the latest video coding standard, Versatile Video Coding (VVC) achieves up to 40% Bjøntegaard delta bit-rate (BD-rate) reduction compared with High Efficiency Video Coding (HEVC). Recently, Convolutional Neural Network (CNN) has attracted tremendous attention and shows great potential in video coding. In this paper, we design a Multi-Density Convolutional Neural Network (MDCNN) as an integrated in-loop filter to improve the quality of the reconstructed frames.