论文标题
Lenslet Light Field图像编码：分类，审查和评估
Lenslet Light Field Image Coding: Classifying, Reviewing and Evaluating
论文作者
论文摘要
近年来，视觉传感器一直在迅速改善，特别是针对视觉场景中存在的光的富裕收购。在这种情况下，所谓的透镜光场（LLF）摄像机能够通过使用每个像素位置的方向光测量来丰富视觉表示，可以超越常规的2D视觉采集模型。 LLF成像与大量数据相关，因此可以部署涉及传输和存储的应用程序的有效编码解决方案。因此，近年来已经在开发越来越有效的LLF成像编码（LLFIC）解决方案方面进行了大量研究工作。在这种情况下，本文的主要目的是在文献中审查和评估文献中一些最相关的LLFIC解决方案，并以新颖的分类分类法指导，这可以更好地组织这一领域。这样，就可以就当前的LLFIC现状得出更扎实的结论，从而更好地推动该技术领域的未来研究和标准化发展。
In recent years, visual sensors have been quickly improving, notably targeting richer acquisitions of the light present in a visual scene. In this context, the so-called lenslet light field (LLF) cameras are able to go beyond the conventional 2D visual acquisition models, by enriching the visual representation with directional light measures for each pixel position. LLF imaging is associated to large amounts of data, thus critically demanding efficient coding solutions in order applications involving transmission and storage may be deployed. For this reason, considerable research efforts have been invested in recent years in developing increasingly efficient LLF imaging coding (LLFIC) solutions. In this context, the main objective of this paper is to review and evaluate some of the most relevant LLFIC solutions in the literature, guided by a novel classification taxonomy, which allows better organizing this field. In this way, more solid conclusions can be drawn about the current LLFIC status quo, thus allowing to better drive future research and standardization developments in this technical area.
