JPEG-matched data filling of sparse images

To efficiently compress rasterized compound documents, an encoder must be content-adaptive. Content adaptivity may be achieved by using a layered approach. In such an approach, a compound image is segmented into layers and then the appropriate encoders are employed to compress these layers individually. A major factor in using these standard encoders efficiently is to match the layer characteristics to these of the encoder by using data filling techniques to fill-in the initially sparse layers. In this work, we propose a sub-optimal non-linear projections scheme that efficiently matches the baseline JPEG coder in compressing background layers, leading to smaller files with better image quality.