Through the development of medical imaging systems and their integration into a complete information system, the need for advanced joint coding and network services becomes predominant. PACS (Picture Archiving and Communication System) aims to acquire, store and compress, retrieve, present and distribute medical images. These systems have to be accessible via the Internet or wireless channels. Thus protection processes against transmission errors have to be added to get a powerful joint source-channel coding tool. Moreover, these sensitive data require confidentiality and privacy for archiving and transmission purposes, leading to use cryptography and data embedding solutions. This chapter introduces data integrity protection and developed dedicated tools of content protection and secure bitstream transmission for medical encoded image purposes. In particular, the LAR image coding method is defined together with advanced securization services.

When considering color images and more generally multi component images, state of the art image codecs usually achieve component decorrelation through static color transforms such as YUV or YCoCg. This approach leads to suboptimal results as statistics of the image are not taken into account. The new approach proposed here offers to remove the correlation of one component according to another adaptively during the prediction process of an image codec. Through two jointly used processes, one aiming at choosing the best predictor of a component and another aiming at improving the predictor's effectiveness, this new approach improves both image quality and compression ratio. This new technique has been applied to the LAR codec and shows an improvement over previous studies up to 20% in rate and 0.5db in PSNR at low bit rates.

The LAR codec is an advanced image compression method relying on a quadtree partitioning of the image. The partitioning strongly impacts the LAR codec efficiency and enables both compression and representation efficiency. In order to increase the perceptual representation abilities without penalizing the compression efficiency we introduce and evaluate two partitioning criteria working in the Lab color space. These criteria are confronted to the original criterion and their compression and robustness performances are analyzed.

Next generations of still image codecs should not only have to be efficient in terms of compression ratio, but also propose other functionalities such as scalability, lossy and lossless capabilities, region-of-interest coding, etc. In previous works, we have proposed a scalable compression method called LAR, for Locally Adaptive Resolution, that covers these requirements. In particular, the Interleaved S+P scheme offers an efficient mean to compress images. In this paper, three modifications of this coder are proposed to extend its capabilities to the lossless coding of colour images. Firstly, decorrelation of the image components is introduced by using reversible colour transforms. Secondly, an adaptive decorrelation of the components is introduced. Finally, a classification between the image components is introduced. Results are then discussed and compared to the state of the art, thus revealing high compression performances of our coding solution.

Quality assessment is of major importance when designing and testing an image/video coding technique. Compression performances are usually evaluated by means of rate-distortion curves. However, the PSNR is commonly employed as the distortion measure. We hereby present a full quality assessment benchmark for the LAR (locally adaptive resolution) coder. We conducted a subjective experiment, where nineteen observers were asked to assess the perceptual quality of LAR coded images under normalized viewing conditions. Furthermore, five objective quality assessment metrics were used in order to determine the most suitable metric for the LAR coder. Finally, both JPEG and JPEG200 images were generated and assessed during the subjective experiment in order to define the optimal quality metric which should be used when comparing the codecs' output images quality.

The JPEG committee has initiated a study of potential technologies dedicated to future generation image compression systems. The idea is to design a new norm of image compression, named JPEG AIC (Advanced Image Coding), together with advanced evaluation methodologies, closely matching to human vision system characteristics. JPEG AIC thus aimed at defining a complete coding system able to address advanced functionalities such as lossy to lossless compression, scalability (spatial, temporal, depth, quality, complexity, component, granularity...), robustness, embed-ability, content description for image handling at object level... The chosen compression method would have to fit perceptual metrics defined by the JPEG community within the JPEG AIC project. In this context, we propose the Locally Adaptive Resolution (LAR) codec as a contribution to the relative call for technologies, tending to fit all of previous functionalities. This method is a coding solution that simultaneously proposes a relevant representation of the image. This property is exploited through various complementary coding schemes in order to design a highly scalable encoder. The LAR method has been initially introduced for lossy image coding. This efficient image compression solution relies on a content-based system driven by a specific quadtree representation, based on the assumption that an image can be represented as layers of basic information and local texture. Multiresolution versions of this codec have shown their efficiency, from low bit rates up to lossless compressed images. An original hierarchical self-extracting region representation has also been elaborated: a segmentation process is realized at both coder and decoder, leading to a free segmentation map. This later can be further exploited for color region encoding, image handling at region level. Moreover, the inherent structure of the LAR codec can be used for advanced functionalities such as content securization purposes. In particular, dedicated Unequal Error Protection systems have been produced and tested for transmission over the Internet or wireless channels. Hierarchical selective encryption techniques have been adapted to our coding scheme. Data hiding system based on the LAR multiresolution description allows efficient content protection. Thanks to the modularity of our coding scheme, complexity can be adjusted to address various embedded systems. For example, basic version of the LAR coder has been implemented onto FPGA platform while respecting real-time constraints. Pyramidal LAR solution and hierarchical segmentation process have also been prototyped on DSPs heterogeneous architectures. This chapter first introduces JPEG AIC scope and details associated requirements. Then we develop the technical features, of the LAR system, and show the originality of the proposed scheme, both in terms of functionalities and services. In particular, we show that the LAR coder remains efficient for natural images, medical images, and art images.

Next generations of still image codecs should not only have to be efficient in terms of compression ratio, but also propose other functionalities such as scalability, lossy and lossless abilities, region of interest coding, etc. In previous works, we have proposed the LAR compression method covering these requirements. In particular, the RWHaT + P pyramid has recently been presented as a powerful reversible scalable coding technique. This paper introduces new significant improvements by the use of an inter-coefficient classification method. Results are discussed and compared to the state of the art.

Next generations of still image codecs should not only have to be efficient in terms of compression ratio, but also propose other functionalities such as scalability, lossy and lossless abilities, region of interest coding, etc. In previous works, we have proposed the LAR compression method covering these requirements. In particular, the Interleaved S+P scheme offers an efficient mean to compress images, especially in the medical field. In this paper, two classification methods are proposed in order to increase the compression ratio of the coder. The first one is based on a spatial context, the second one takes into account the local activity of the picture. Results are then discussed and compared to the state of the art, thus revealing high compression performances of our coding solution.

Next generations of still image codecs should not only have to be efficient in terms of compression ratio, but also propose other functionalities such as scalability, lossy and lossless abilities, region of interest coding, etc. In previous works, we have proposed the LAR compression method covering these requirements. In particular, the Interleaved S+P scheme offers an efficient mean to compress images, especially in the medical field. In this paper, two classification methods are proposed in order to increase the compression ratio of the coder. The first one is based on a spatial context, the second one takes into account the local activity of the picture. Results are then discussed and compared to the state of the art, thus revealing high compression performances of our coding solution.

EROS is the largest database in the world of high resolution art pictures. The TSAR project is designed to open it in a secure, efficient and user-friendly way that involves cryptography and watermarking as well as compression and region-level representation abilities. This paper more particularly addresses the two last points. The LAR codec is first presented as a suitable solution for picture encoding with compression ranging from highly lossy to lossless. Then, we detail the concept of self-extracting region representation, which consists of performing a segmentation process at both the coder and decoder from a highly compressed image, and later locally enhancing the image in a region of interest. The overall scheme provides an efficient, consistent solution for advanced data browsing.

We present an efficient content-based image coding called locally adaptive resolution (LAR) offering advanced scalability at different semantic levels, i.e., pixel, block, and region. A local analysis of image activity leads to a nonuniform block representation supporting two layers of image description. The first layer provides global information encoded in the spatial domain enabling a low bit rate while preserving contours. The second layer holds texture information encoded in the spectral domain, enabling scalable bitstream in accordance with the required quality. This basic LAR coding leads to an efficient progressive compression, evaluated through subjective quality tests. Its nonuniform block representation also allows a hierarchical region representation providing higher semantic functionalities. More precisely, the segmentation process can be simultaneously performed at both the coder and the decoder from only the luminance component highly compressed by the first coding layer. This solution provides a representation at a region level while avoiding any contour encoding overhead. Region enhancement can then be realized through the second layer. Furthermore, very high compression of the chromatic components is achieved thanks to this region representation. In this scheme, a low-cost chromatic control, which was first introduced during the segmentation process, increases the consistency of region representation in terms of color.

Cet article presente un schema original de codage progressif d'images couleur apportant a la fois une efficacite en termes de compression (meilleure qualite subjective que Jpeg2000) et des fonctionnalites au niveau region a bas debits pour le codeur et le decodeur. A partir de l'image Y des luminances codee a bas debit par le codec LAR (Locally Adaptive Resolution), une description en regions, sans codage des contours, est obtenue a travers un procede de segmentation effectue au codeur et au decodeur. Cette segmentation peut etre controlee par les composantes chromatiques pour une meilleure coherence du resultat d'un point de vue couleur. Un codage base regions applique sur les images de chrominance produit alors une compression de ces composantes a tres bas debit. Comme les regions et le codage de leur contenu partagent une meme grille de representation, l'amelioration de la qualite de l'image peut etre globale, ou limitee a une zone d'interet.

This LAR (Locally Adaptive Resolution) color image coding scheme yields to an efficient progressive compression with a better subjective quality than Jpeg2000. Additionally, it offers region functionalities for low bit rate coding and decoding. From highly compressed luminance, a region description, without contours encoding, can be obtained through a segmentation process performed at both coder and decoder. Considering color results, controlled chrominance components segmentation provides a better data consistency simultaneously with a low bit rate compression. As regions and their encoding are based on a same representation grid, enhancement of image quality can be global, or only restricted to a Region Of Interest.

This paper presents a very efficient solution for chromatic image coding using a region technique. The originality relies in the fact that the region's description is not transmitted, but deduced from the low resolution luminance representation, and thus does not induce any overhead. The Y component is compressed by the basic LAR (locally adaptive resolution) codec, a content-based approach composed of a spatial coder, to achieve low bit rates, and a spectral coder, to compress the error image. The two coders, as well as the region description, share the same data representation structure, avoiding the contour or block artifacts of traditional techniques

This paper presents a full compression decompression algorithm for a distributed videoconference system via the Internet. It is based on a new method called LAR. This algorithm mixes both spatial and spectral approaches. The spatial coder provides a low resolution image but preserves object boundaries, whereas the spectral coder can add local texture information. The encoding scheme is progressive, providing great flexibility for rate/quality trade-offs.

This paper presents a full codec for a distributed video surveillance system. It is based on a new method called LAR, mixing both spatial and spectral approaches. The spatial coder provides a low resolution image but preserving objects boundaries, whereas the spectral coder can add the local texture information. The encoding scheme is progressive, providing large flexibility for rate/quality trade-off.

In order to achieve a color image coding based on the human visual system features, we have been interested by the design of a perceptually based quantizer. The cardinal directions Ach, Cr1 and Cr2, designed by Krauskopf from habituation experiments and validated in our lab from spatial masking experiments, have been used to characterize color images. The achromatic component, already considered in previous study, will not be considered here. The same methodology has been applied to the two chromatic components to specify the decision thresholds and the reconstruction levels which ensure that the degradations induced will be lower than their visibility thresholds. Two observers have been used for each of the two components. From the values obtained for Cr1 component one should notice that the decision thresholds and reconstruction levels follow a linear law even at higher levels. However, for Cr2 component the values seem following a monotonous increasing function. To determine if these behaviors are frequency dependent, further experiments have been conducted with stimulus frequencies varying from 1cy/deg to 4cy/deg. The measured values show no significant variations. Finally, instead of sinusoidal stimuli, filtered textures have been used to take into account the spatio-frequential combination. The same laws (linear for Cr1 and monotonous increasing for Cr2) have been observed even if a variation in the quantization intervals is reported.