We present an efficient content-based image coding called LAR (Locally Adaptive Resolution) offering advanced scalability at different semantic levels i.e. pixel, block and region. A local analysis of image activity leads to a non-uniform block representation supporting two layers of image description. The first layer provides global information encoded in the spatial domain enabling low bit rate while preserving contours. The second layer holds texture information encoded in the spectral domain enabling scalable bitstream in accordance with the required quality. This basic LAR coding leads to an efficient progressive compression, evaluated through subjective quality tests. Its non-uniform block representation also allows a hierarchical region representation providing higher semantic functionalities. More precisely, the segmentation process can be simultaneously performed at both the coder and the decoder from only the luminance component highly compressed by the first coding layer. This solution provides a representation at a region level while avoiding any contour encoding overhead. Region enhancement can then be realized through the second layer. Furthermore, very high compression of the chromatic components is achieved thanks to this region representation. In this scheme, a low-cost chromatic control, first introduced during the segmentation process, increases the consistency of region representation in terms of color.