In a resolution scalable image coding algorithm, a multiresolution representation of the data is often obtained using a linear filter bank. Reversible cellular automata have been recently proposed as simpler, nonlinear filter banks that produce a similar representation. The original image is decomposed into four subbands, such that one of them retains most of the features of the original image at a reduced scale. In this paper, we discuss the utilization of reversible cellular automata and arithmetic coding for scalable compression of binary and grayscale images. In the binary case, the proposed algorithm that uses simple local rules compares well with the JBIG compression standard, in particular for images where the foreground is made of a simple connected region. For complex images, more efficient local rules based upon the lifting principle have been designed. They provide compression performances very close to or even better than JBIG, depending upon the image characteristics. In the grayscale case, and in particular for smooth images such as depth maps, the proposed algorithm outperforms both the JBIG and the JPEG2000 standards under most coding conditions.

In this paper, we study the use of GF(q)-quantized LDGM codes for binary source coding. By employing quantization, it is possible to obtain binary codewords with a non-uniform distribution. The obtained statistics is hence suitable for optimal, direct quantization of non-uniform Bernoulli sources. We employ a message-passing algorithm combined with a decimation procedure in order to perform compression. The experimental results based on GF(q)-LDGM codes with regular degree distributions yield performances quite close to the theoretical rate-distortion bounds.

Spatially scalable image coding algorithms are mostly based on linear filtering techniques that give a multi-resolution representation of the data. Reversible cellular automata can be instead used as simpler, non-linear filter banks that give similar performance. In this paper, we investigate the use of reversible cellular automata for lossy to lossless and spatially scalable coding of smooth multi-level images, such as depth maps. In a few cases, the compression performance of the proposed coding method is comparable to that of the JBIG standard, but, under most test conditions, we show better compression performances than those obtained with the JBIG or the JPEG2000 standards. The results stimulate further investigation into cellular automata-based methods for multi-level image compression.

Reversible cellular automata have been proposed as nonlinear filter banks for spatially scalable binary image compression. Previous design approaches led to efficient coders for simple images but do not guarantee detail preservation across scales. In this work we are concerned with the design of optimized cellular automata rules that offer better performances. The lifting principle is used for guaranteeing reversibility. We obtain a scalable coder that (i) offers the same quality of JBIG in the reduced scale images, (ii) achieves high compression ratios on simple images, (iii) provides at least the same performance of JBIG on any image content, and (iv) employs arithmetic coding with a very small number of contexts for reduced complexity.

At mid to low bit-rates, the standard DCT based video codecs produce, in the reconstructed sequences, annoying artifacts known as ringing and blocking effects. We are concerned in blocking artifact removal with lapped embedded IDCT. In intra frames, the deblocking always produces an increase in the peak signal-to-noise ratio; as a consequence of the processing on the last reference intra frame, inter frames, whose prediction error is decoded with IDCT, are free of blocking artifacts. Using the proposed algorithm on the inter frame decreases the resulting PSNR while improving the visual quality of the decoded images. We present a theoretical bound to the PSNR reduction.