Scanning orders of bitplane image coding engines are commonly envisaged from theoretical or experimental insights and assessed in practice in terms of coding performance. This paper evaluates classic scanning strategies of modern bitplane image codecs using several theoretical-practical mechanisms conceived from rate-distortion theory. The use of these mechanisms allows distinguishing those features of the bitplane coder that are essential from those that are not. This discernment can aid the design of new bitplane coding engines with some special purposes and/or requirements. To emphasize this point, a low-complexity scanning strategy is proposed. Experimental evidence illustrates, assesses, and validates the proposed mechanisms and scanning orders.

This paper introduces a probability model for symbols emitted by bitplane image coding engines, which is conceived from a precise characterization of the signal produced by a wavelet transform. Main insights behind the proposed model are the estimation of the magnitude of wavelet coefficients as the arithmetic mean of its neighbors' magnitude (the so-called local average), and the assumption that emitted bits are under-complete representations of the underlying signal. The local average-based probability model is introduced in the framework of JPEG2000. While the resulting system is not JPEG2000 compatible, it preserves all features of the standard. Practical benefits of our model are enhanced coding efficiency, more opportunities for parallelism, and improved spatial scalability.

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This study measures the effect of lossy image compression on the digital classification of crops and forest areas. A hybrid classification method using satellite images and other variables has been used. The results contribute interesting new data on the influence of compression on the quality of the produced cartography, both from a "by pixel" perspective and regarding the homogeneity of the obtained polygons. The classified area in classifications only carried out with radiometric variables or with NDVI and humidity (for crops) increases as image compression increases, although the increase is smaller for JPEG2000 formats and for crops. On the other hand, the classified area decreases in classifications which also take into account topoclimatic variables (for forests). Overall image accuracy diminishes at high compression ratios (CR), although the point of inflection occurs at different CR depending on the compression format. As a rule, the JPEG2000 format gives better results quantitatively for forests (accuracy and classified area) and visually (images with less "salt and pepper" effect) for both land covers.

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