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The 3D extension of High Efficiency Video Coding (3D-HEVC) achieves excellent performance for 3D video coding while possessing significant computational complexity. To accelerate the time-consuming coding process of the depth map, a fast algorithm via XGBoost is proposed in this paper. Specifically, a total of 14 specialized XGBoost models are used for different block sizes and viewpoint types to achieve early coding unit partition determination (ECP) and early prediction unit mode selection (EPM) to avoid executing the exhaustive traversal coding process.

High Framerate (HFR) video streaming enhances the viewing experience and improves visual clarity. However, it may lead to an increase of both encoding time complexity and compression artifacts at lower bitrates. To address this challenge, this paper proposes a content-aware frame dropping algorithm (CODA) to drop frames uniformly in every video (segment) according to the target bitrate and the video characteristics.

This paper presents experimental results related to adaptive video content mapping used as a compression tool for HDR-PQ content. The purpose of adaptive video content mapping is to adapt the video signal dynamically depending on its statistical properties in order to better exploit the signal codewords range. Adaptive video content mapping has been investigated during the Versatile Video Coding (VVC) standard development with two main implementation designs: in-loop mapping, and out-of-loop mapping.

We propose a talking face video compression framework by implicitly transforming the temporal evolution into compact feature representation. More specifically, the temporal evolution of faces, which is complex, non-linear and difficult to extrapolate, is modelled in an end-to-end inference framework based upon very compact features. This enables the high-quality rendering of the face videos, which benefits from the learning of dense motion map with compact feature representation.

As a new signal processing technology, compressed sensing (CS) has been showed to be a promising solution for compressing cipher images. However, the previous CS-based schemes are unsatisfactory in terms of ratio-distortion (R-D) performance. In order to solve this problem, an image encryption-then-compression (ETC) scheme by using semi-tensor product CS (STP-CS) and pre-mapping is proposed in this paper. In the proposed scheme, the original image is encrypted by using the scrambling operation. After image encryption, the cipher image is compressed through three steps.