This paper presents a new lossless binary image compression method. The method consists of four stages: preprocessing, quadtree compressing, run length compressing and statistical model-based compressing. The preprocessing stage is to reduce the entropy of a binary image. In the quadtree compressing stage, a breadth first traversal (BFT) linear quadtree is used to compress the image. The run length compressing stage uses the run length method to encode the tree list and colour list of the BFT linear quadtree. The statistical model-based compressing stage applies the Huffman coding algorithm to compress the remaining data of the BFT linear quadtree further. Experimental results show that this method can provide an impressive compression result

This paper proposes an image hiding method which first compresses a secret image by base switching transformation (BST) and then ranks the compression data according to their significance. Meanwhile, the cover image is transformed into a frequency domain image by discrete wavelet transformation. After that, the dominant compression data are hidden in the lower frequency sub-bands and the trivial compression data in the higher frequency sub-bands of the frequency domain image. When the dominant compression data are damaged, the hamming code method is used to correct the damaged data. If the trivial compression data are changed, only the secret image data relating to the damaged data are affected. This proposed method also can yield a high hiding capacity of 2.25 bits/pixels