  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.
