In the digital prepress workflow, images are represented in the CMYK colour space. Lossy image compression alleviates the need for high storage and bandwidth capacities, resulting from the high spatial and tonal resolution. After the image has been printed on paper, the introduced visual quality loss should not be noticeable to a human observer. Since visual image quality depends on the compression algorithm both quantitatively and qualitatively, and since no visual image quality models incorporating the end‐to‐end image reproduction process are satisfactory, an experimental comparison is the only viable way to quantify subjective image quality. This paper presents the results from an intensive psychovisual study based on a two‐alternative forced‐choice approach involving 164 people, with expert and non‐expert observers distinguished. The primary goal is to evaluate two previously published adaptations of JPEG to CMYK images, and to determine a visually lossless compression ratio threshold for typical printing applications. The improvements are based on tonal decorrelation and overlapping block transforms. Results on three typical prepress test images indicate that the proposed adaptations are useful and that for the investigated printing configuration, compression ratios up to 20 can be used safely.

CMYK color images are used extensively in prepress applications. When compressing those color images one has to deal with four different color channels. Usually compression algorithms only take into account the spatial redundancy that is present in the image data. This approach does not yield an optimal data reduction since there also exists a high correlation between the different colors in natural images. This paper shows that a significant gain in data reduction can be achieved by exploiting this color redundancy. Some popular transform coders, including DCT‐based JPEG and the SPIHT wavelet coder, were used for reducing the spatial redundancy. The performance of the algorithms was evaluated using a quality criterion based on human perception like the mean CIEL*a*b*ΔE error.

This paper shows that an n x 1 integer vector can be exactly recovered from its Hadamard transform coefficients, even when 0.5 n log(2)(n) of the (less significant) bits of these coefficients are removed. The paper introduces a fast "lossless" dequantization algorithm for this purpose. To investigate the usefulness of the procedure in data compression, the paper investigates an embedded block image coding technique called the "LHAD" based on the algorithm. The results show that lossless compression ratios close to the state of the art can be achieved, but that techniques such as CALIC and S+P still perform better.

Applications such as printing on demand and personalized printing have arisen where lossless halftone image compression can be useful for increasing transmission speed and lowering storage costs. We present an improvement on the context modeling scheme by adapting the context template to the periodic structure of the halftone image. This is a non-trivial problem for which we propose a fast close-to-optimal context template selection scheme based on the calculation and sorting of the autocorrelation function on a part of the image. For evaluating our scheme, we have also investigated the compression performance of a suboptimal approach based on an incremental exhaustive search; this approach can be used in practice for decompression but not for compression because of timing requirements. We have experimented with classical halftones of different resolutions and sizes and screened under different angles.

The huge sizes of screened colour-separated photographic images makes lossless compression very beneficial for both storage and transmission. Because of the special structure induced by the half-tone dots, the compression results obtained on the CCITT test images might not apply to high-resolution screened images and the default parameters of existing compression algorithms may not be optimal. In this paper we compare the performance of different classes of lossless coders: general-purpose one-dimensional coders, non-adaptive two-dimensional black-and-white coders and adaptive two-dimensional coders. Firstly, experiments on a set of test images screened under different conditions showed that MGBILEVEL and JBIG perform best with respect to compression efficiency; the difference with the other coders is significant. Secondly, we investigated the influence of the screening method (stochastic or classical screening) and screening resolution on the compression ratio for these techniques.

This paper presents a lossless block-transform coder based on the Hadamard transform that achieves a compression ratio which is as good as that of the current lossless JPEG-standard and about 10 to 20% lower than the currently best state-of-the-art technique Calic. The main advantage of the proposed technique is that it produces an embedded data stream which may be truncated to achieve lossy compression. Preliminary results obtained on the "lena" image show that, at compression ratios up to 10, the proposed scheme (somewhat surprisingly) produces a better image quality (both visually and in terms of rms error) than lossy JPEG. This means that the proposed coder performs well over a wide range of compression ratios and is therefore a useful alternative for the lossy and lossless JPEG-standards in nearly-lossless coding or when lossless and lossy decoding must be supported simultaneously. Finally, the transform part of the proposed scheme requires fewer computations than lossy JPEG (and only slightly more than lossless JPEG).