Image-based rendering techniques stand at the core of an immersive experience for the user, as they generate novel views given a set of multiple input images. Since they have shown good performance in terms of objective and subjective quality, the research community devotes great effort to their improvement. However, the large volume of data necessary to render at the receiver's side hinders applications in limited bandwidth environments or prevents their employment in real-time applications. We present LeHoPP, a method for input pixel pruning, where we examine the importance of each input pixel concerning the rendered view, and we avoid the use of irrelevant pixels. Even without retraining the image-based rendering network, our approach shows a good trade-off between synthesis quality and pixel rate. When tested in the general neural rendering framework, compared to other pruning baselines, LeHoPP gains between $0.9$ dB and $3.6$ dB on average.

翻译：基于图像的渲染技术是用户沉浸式体验的核心，它通过一组多张输入图像生成新视角。由于该技术在客观和主观质量方面表现出色，研究界投入大量精力对其进行改进。然而，在接收端渲染所需的大量数据限制了其在有限带宽环境中的应用，或阻碍了其实时应用的部署。我们提出LeHoPP，一种输入像素裁剪方法，该方法评估每个输入像素对渲染视图的重要性，并避免使用无关像素。即使不重新训练基于图像的渲染网络，我们的方法也能在合成质量与像素率之间取得良好平衡。在通用神经渲染框架中测试时，与其他裁剪基线方法相比，LeHoPP的平均增益达到0.9分贝至3.6分贝。