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Optical-Flow Based Nonlinear Weighted Prediction for SDR and Backward Compatible HDR Video Coding

机译:基于光流的SDR和向后兼容HDR视频编码的非线性加权预测

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摘要

Tone Mapping Operators (TMO) designed for videos can be classified into two categories. In a first approach, TMOs are temporal filtered to reduce temporal artifacts and provide a Standard Dynamic Range (SDR) content with improved temporal consistency. This however does not improve the SDR coding Rate Distortion (RD) performances. A second approach is to design the TMO with the goal of optimizing the SDR coding rate-distortion performances. This second category of methods may lead to SDR videos altering the artistic intent compared with the produced HDR content. In this paper, we combine the benefits of the two approaches by introducing new Weighted Prediction (WP) methods inside the HEVC SDR codec. As a first step, we demonstrate the interest of the WP methods compared to TMO optimized for RD performances. Then we present the newly introduced WP algorithm and WP modes. The WP algorithm consists in performing a global motion compensation between frames using an optical flow, and the new modes are based on non linear functions in contrast with the literature using only linear functions. The contribution of each novelty is studied independently and in a second time they are all put in competition to maximize the RD performances. Tests were made for HDR backward compatible compression but also for SDR compression only. In both cases, the proposed WP methods improve the RD performances while maintaining the SDR temporal coherency.
机译:专为视频设计的音调映射运算符(TMO)可以分为两类。在第一种方法中,TMOS是时间滤波,以减少时间伪像并提供具有改进的时间一致性的标准动态范围(SDR)内容。然而,这不会改善SDR编码率失真(RD)性能。第二种方法是设计TMO,其目的是优化SDR编码率失真性能。与所产生的HDR内容相比,该第二类方法可能导致SDR视频改变艺术意图。在本文中,我们通过在HEVC SDR编解码器内引入新的加权预测(WP)方法来结合两种方法的益处。作为第一步,我们展示了与RD性能优化的TMO相比的WP方法的兴趣。然后我们介绍了新推出的WP算法和WP模式。 WP算法包括使用光流的帧之间执行全局运动补偿,并且新模式基于非线性函数与仅使用线性函数的文献相反。每个新奇的贡献都是独立研究的,在第二次中,他们都在竞争中,以最大化RD表演。测试是为HDR向后兼容压缩而制作的,但仅用于SDR压缩。在这两种情况下,所提出的WP方法改善了RD性能,同时保持了SDR时间一致性。

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