Unequal compressive imaging

Recently, novel compressive sensing (CS) techniques have been employed to concurrently perform compression and image sampling. Since an image has sparse representation in some proper transform basis, such as discrete cosine transform (DCT) and wavelet transform, we can reconstruct it from its undersampled random projections called measurements employing CS techniques. We consider the fact that the area in an image that contains the main subject, such as the face in a portrait, is more important to viewers. We employ an existing algorithm from image processing area to find the area of the images that corresponds the main subject, and propose to directly apply unequal compressive sampling on coefficients of this area. With this setup, the main subject is reconstructed with a higher accuracy, while the less important areas are slightly degraded. Unequal compressive imaging is mainly inspired by a previous work by Rahnavard et al. on unequal error protection rateless codes.