> Whole point of hashing algorithms is that they produce unique hashes for different inputs. Wouldn't a larger input value (bits of the image) make it harder to have collisions?
Standard cryptographic hashes should have certain properties like https://en.wikipedia.org/wiki/Avalanche_effect
However, a perceptual hash desires the opposite. Due to the pigeonhole principle, a larger space mapping to a smaller space, involves more collisions. In fact, all hashes have infinite collisions, one just tries to design hashes that don't have many collisions on smaller length inputs. Ultimately though, there will be at least 2^n collisions for all inputs n bits long if the hash is n bit output. You can easily calculate this by looking at the excess in size between input space size and output space size.
https://en.wikipedia.org/wiki/Pigeonhole_principle
Perceptual hashes are essentially designed to collide on similar data. The fine details are lost. An ideal perceptual hash algorithm would quantize as many alterable properties of an image as possible. Contrast, brightness, edges, hue, fine details, etc. In the end, you have a bunch of splotches in a certain composition that form the low dimensional eigenbasis of the hash.