Chanel-Orderer: A Channel-Ordering Predictor for Tri-Channel Natural Images

TL;DR

Chanel-Orderer is a novel model that predicts the correct channel order in permuted 3-channel images and can distinguish near-gray-scale images, inspired by human visual perception.

Contribution

It introduces a new architecture with inductive biases to accurately predict channel order and identify monochromatic images in natural scenes.

Findings

Successfully predicts correct channel order in permuted images.

Can distinguish near-gray-scale from polychromatic images.

Mimics human visual perception of natural colors.

Abstract

This paper shows a proof-of-concept that, given a typical 3-channel images but in a randomly permuted channel order, a model (termed as Chanel-Orderer) with ad-hoc inductive biases in terms of both architecture and loss functions can accurately predict the channel ordering and knows how to make it right. Specifically, Chanel-Orderer learns to score each of the three channels with the priors of object semantics and uses the resulting scores to predict the channel ordering. This brings up benefits into a typical scenario where an \texttt{RGB} image is often mis-displayed in the \texttt{BGR} format and needs to be corrected into the right order. Furthermore, as a byproduct, the resulting model Chanel-Orderer is able to tell whether a given image is a near-gray-scale image (near-monochromatic) or not (polychromatic). Our research suggests that Chanel-Orderer mimics human visual coloring of our physical natural world.