Visually lossless image compression extension for JPEG based on just-noticeable distortion evaluation

A visually lossless image encoding extension for JPEG is presented. Such extension enables an efficient implementation of perceptual coding by reusing existing widespread software libraries and hardware IP cores for JPEG. For any pixel in a decoded image, the proposed algorithm guarantees a maximum distortion bounded by the just-noticeable distortion (JND) threshold measured based on the input image. Perceptual coding is performed in three steps: (1) standard transform domain coding, (2) spatial domain distortion visibility analysis by JND model and (3) spatial domain residual coding. Such scheme has been implemented in this work as an extension for JPEG based on a low complexity JND model. The encoder determines if a pixel block in a standard JPEG output image contains distortions beyond the visibility threshold given by the JND model. If it is true then the locations and the values of such distortions are encoded as side information. Quantization step size for the distortion values, i.e. perceptual residuals, are chosen based on the visibility threshold. Experimental results show that in terms of compression efficiency, the proposed perceptual encoding extension outperforms the standard JPEG encoder by 50% for a visually lossless compression of images.