The quality of visual input impacts both human and machine perception. Consequently many processing techniques exist that deal with different distortions. Usually they are applied freely and unsupervised. We propose a novel method called CD2 to protect against errors that arise during image processing. It is based on distributions of image contrast and custom distance functions which capture the effect of noise, compression, etc. CD2 achieves excellent performance on image quality analysis benchmarks and in a separate user test with only a small data and computation overhead.