It is well known that color formation acts as a noise-reducing lossy compression mechanism that results in ambiguity, known as metamerism. Surfaces that match under one set of conditions-an illuminant and observer or capture device-can mismatch under others. The phenomenon has been
studied extensively in the past, leading to important results like metamer mismatch volumes, color correction, reflectance estimation and the computation of metamer sets-sets of all possible reflectances that could result in a given sensor response. However, most of these approaches have three
limitations: first, they simplify the problem and make assumptions about what reflectances can look like (i.e., being smooth, natural, residing in a subspace based on some measured data), second, they deal with strict mathematical metamerism and overlook noise or precision, and third, only
isolated responses are considered without taking the context of a response into account. In this paper we address these limitations by outlining an approach that allows for the robust computation of approximate unconstrained metamer sets and exact unconstrained paramer sets. The notion of
spatial or relational paramer sets that take neighboring responses into account, and applications to illuminant estimation and color constancy are also briefly discussed.