Digital images are becoming increasingly successful thanks to the development and the facilitated access to systems permitting their generation (i.e. camera, scanner, imaging software, etc). A digital image basically corresponds to a 2D discrete set of regularly spaced samples, called pixels, where each pixel contains the light intensity information (e.g., luminance, chrominance) of a very localized spatial region of the image. In the case of natural images, pixel values are acquired through one or several arrays of MOS semiconductors (Charge Couple Devices, CCDs), each generating an electrical information proportional to the incoming light intensity. The initial finalities of digital images were the storage on a dedicated medium (e.g., camera's memory, computer's hard drive, CDROM), eventual transmissions, and final display on a screen or printing. With such a narrow scope, the principal goal of image processing and coding tools was to face storage and transmission bandwidth limitations thanks to efficient compression algorithms reducing the image representation size. However, with recent developments in computing, algorithmic and telecommunication domains, many new applications (i.e. web-publishing, remote browsing etc.) have arisen. They generally require additional and enhanced features (i.e. progressive decoding, random-access, region of interest support, robustness to transmission errors, etc.) and have motivated the creation of a new generation of coding algorithms which, besides their good compression performance, present many other useful features. Hence, digital images are almost never represented as a simple set of pixel values (i.e. raw representation) but, instead, under a specific compact way (i.e. compressed or coded representation), chosen according to features it brings to the considered application. A compressed version of an image is obtained by removing as much spatial, visual and statistical redundancies as possible, thanks to appropriate coding methods, while keeping an acceptable visual quality. Noting that natural images have most of their energy concentrated in low frequency components, recent coding algorithms generally first decompose the image into a specific frequency domain DCT, DWT, etc). The goal is to obtain a representation, where few coefficients are sufficient for reconstructing the image with a good quality. The precision of transformed coefficients is then generally reduced by quantization in order to make them more compressible by an entropy coder, aiming at removing statistical redundancies of quantization indexes. The ultimate compressed representation, called codestream, is usually obtained by a rate-allocation process that tries to achieve the best trade-off between the compression ratio and the reconstructed image quality. JPEG 2000, the new still image coding standard developed by the Joint Photographic Experts Group JPEG), is based on these state-of-the-art compression techniques, but is also designed to fulfill many requirements of recent applications. It only normalizes the decoding algorithm and consequently let some liberty for designing encoders optimized for some specific features or for building extensions that take profit from its compressed domain specifics. The success of the JPEG 2000 standard will not only depend on its intrinsic performance, but also on its ability to comply with specific demands of actual and future imaging applications. Thus, among the major concerns of image content providers are the security of the transmission over networks and of the image itself: with current facilities to instantly access on-line digital libraries from anywhere in the world, to perfectly copy and to easily modify the content of an image, solutions must be found in order to permit, at one hand, Intellectual Property Right (IPR) protection and image integrity verification and, at the other hand, the development of dedicated tools favoring exchange and purchase of images over communication networks. It is worth pointing out that some cryptographic-based solutions to these problems already exist. However, they need to be adapted to the digital image and its compressed domain representation in order to take full advantage from their specifics and to avoid restraining fields of applications. The JPEG 2000 coding algorithm is mainly based on Discrete Wavelet Transform (DWT), embedded scalar quantization and adaptive arithmetic coding. From a terminology point of view, this means that compressed domain representation can indifferently refer to either wavelet coefficients, or quantized wavelet coefficients, or bit streams (i.e. entropy coded group of quantization indexes) or the codestream (i.e. aggregation of bit streams and headers containing necessary decoding information). The choice of the appropriate compressed domain actually depends on the considered application. The quality of a JPEG 2000 image, at a given compression ratio, mainly depends on the rate-allocation procedure used at the encoder side. Such a procedure applies on entropy coded quantization indexes and favors groups of quantization indexes (i.e. code-blocks) offering the best rate-distortion trade-offs. However, this does not necessary correspond to the most interesting part of the image, from an end-observer point of view. Hence, the standard provides with ways to define Regions Of Interest (ROI) which are prioritized during the encoding process in order to exhibit a higher quality than the rest (i.e. background) at any decoding time. This feature applies either in the quantized wavelet domain or at the bit stream level, but its parameters are generally not very explicit for a standard end-user and only provide with a rough control of the decoded ROI quality. Consequently, the first objective of this thesis is to create and also extend compressed domain tools for controlling the quality of a JPEG 2000 ROI. In the meantime, several image processing techniques, such as watermarking, are applied directly in the spatial domain or in a specific transform space defined from the spatial domain. However, since digital images are preferably available under a compressed/encoded format, which we assume to be JPEG 2000 in this thesis, their implementation first imply decompressing the image, then apply the considered processing task and finally re-encode the resulting image. Such a scheme has two main drawbacks: First, it generally implies time and complexity overheads compared to equivalent methods (if they exist) in the JPEG 2000 compressed domain. Such effects become important when the scheme is repeatedly applied on multiple images. Second, since encoding and decoding operations are generally lossy, the introduced distortion can become non negligible whenever several processing tasks are repeated on a same image. These observations lead to the second objective of this thesis, which is to adapt or create a watermarking algorithm dedicated to the JPEG 2000 compressed domain. Finally, there are imaging algorithms, such as authentication and access control, that already exist and could be directly applied to JPEG 2000 images but, because they do not take into account the coding algorithm specifics, they either decrease compression performance or remove useful features of the encoded representation (scalability, random access, etc). This leads to the third objective of this thesis, which is to adapt, create and combine authentication and access control algorithms with JPEG 2000 coding and decoding. Thus, the common goal of the three objectives described above is the deep integration of selected processing and security algorithms into a JPEG 2000 codec in order to provide with minimum complexity, JPEG 2000 compliant codestreams and an unified framework for many imaging applications.