Image Compression using Object-Based Regions of Interest

A new architecture for region of interest (ROI) image coding is proposed. ROIs are defined as image regions containing objects of interest, and an efficient algorithm proposed for the detection of such regions. This algorithm is based on the principle of discriminant saliency, under which salient regions are the image regions of strongest response for a set of features that discriminate the object class of interest from all others. The resulting ROI masks are fully compatible with the JPEG2000 standard. Experimental results are presented for images of complex scenes, which contain both objects and background clutter, demonstrating significant gains for object-based ROI coding, in terms of both subjective image quality and SNR. The proposed ROI-based coder is also shown to be trainable with small, informally collected, image collections (e.g. by simple Web search). This suggests the possibility of user-trained image coders.