In this paper, we present a framework based on a generic representation, which is able to handle most of the radiometric quantities required by global illumination software. A sparse representation in the wavelet space is built using the separation between the directional and the wavelength dependencies of such radiometric quantities. Particularly, we show how to use this representation for spectral power distribution, spectral reflectance and phase function measurements modeling. Then, we explain how the representation is useful for performing spectral rendering. On the one hand, it speeds up spectral path tracing by importance sampling to generate reflected directions and by avoiding expensive computations usually done on-the-fly. On the other hand, it allows efficient spectral photon mapping, both in terms of memory and speed. We also show how complex light emission from real luminaires can be efficiently sampled to emit photons with our numerical model.