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3D-High Efficiency Video Coding (3D-HEVC) is a video compression standard developed for multi-view video plus depth map coding based on the latest HEVC coding standard. We propose an eXtreme Gradient Boosting (XGBoost) system based fast coding unit (CU) level decision for depth maps, which is used to solve the problem of high coding complexity caused by the addition of depth maps and new coding tools in 3D-HEVC. We explore the application of data mining and machine learning in video coding by using texture feature attributes that are highly correlated with CU size.


The just noticeable distortion (JND) has been widely applied in perceptual image/video compression. Yet, the existing JND estimation models are not accurate enough, which results in the degradation of perceptual quality. In this paper, we propose a JND compensation based perceptual video coding (PVC) scheme to compress videos with better perceptual quality. Specifically, a block-level JND estimation model is proposed at first, which leads to this model can be employed in variable block-sizes based video coding directly.


With the rapid development of Internet, short videos draw more and more attentions nowa- days. Due to the small scale of short videos, image-level coding scheme can be applied to improve compression efficiency. In this paper, we propose a densely connected unit based loop filter for short video coding in H.266/VVC. In the proposed loop filter, the densely connected units are specially designed to extract feature maps, and fully decompose videos. By densely connection between layers, the designed units can reuse feature maps, and re- duce the redundancy of features.


Rate distortion optimization (RDO) is the basis for algorithm optimization in video coding, such as mode decision, rate control and etc. Minimizing the rate distortion coding cost is usually employed to determine the optimal coding parameters such as quantization level, coding mode, and etc. However, rate and distortion calculations for optimal solution decision from massive possible candidates suffer from dramatically high computation complexity.


Compressive sensing is a simultaneously signal acquisition and compression technique for efficiently acquiring and reconstructing a signal from a small number of measurements, which can be obtained by linear projections onto sparse signal. In order to further compress the measurements, many works applied intra prediction-based measurement coding. In this paper, we proposed temporal redundancy reduction in compressive video sensing by using moving detection and inter-coding.


We propose a new architecture for distributed image compression from a group of distributed data sources. The work is motivated by practical needs of data-driven codec design, low power consumption, robustness, and data privacy. The proposed architecture, which we refer to as Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC), is able to train distributed encoders and one joint decoder on correlated data sources. Its compression capability is much better than the method of training codecs separately.