Still Image Coding

Still image coding includes compression of binary images and multiple-amplitude level (gray scale or color) images. Substantially different methods are applied for these two cases. This chapter gives a broad overview on different methods, which are in principle combinations of methods for signal decorrelation and analysis, quantization and coding, optimized for the specific characteristics of image signals. For binary images, run-length methods and methods related to conditional entropy coding are most relevant. For multiple-amplitude image signals, vector quantization, predictive coding, transform coding and fractal coding are presented in more detail. Transform coding methods can further be clustered into block transform, filterbank and wavelet transform related methods. These methods are presented mainly for examples of luminance (gray-level) compression, as typically the chrominance components are compressed by the same techniques, but are less challenging in terms of structure and hence will allow higher compression ratios. Building blocks which are necessary to understand the principles of still image coding standards like JPEG and JPEG 2000 are discussed in detail. Further important aspects relate to the robustness of still-image compression methods in the case of transmission losses, and to content-related encoding, which allows to further improve the quality by adaptation to the content properties and signal structure. The basic methods for still image coding are also important as elements within video compression methods, which will be further discussed in chapter 13. When applied to video, still image coding is also denoted as intra- frame coding, expressing that compression of a sequence of video frames is performed without exploiting the interframe redundancies.