Laurent Bédat

Color LAR Codec: A Color Image Representation and Compression Scheme Based on Local Resolution Adjustment and Self-Extracting Region Representation

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.

Scalable lossless and lossy image coding based on the RWHaT+P pyramid and the inter-coefficient classification method

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.

Low bit-rate compression based on LAR imethod for videoconference via Internet

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.

Locally Adaptive Resolution (LAR) codec

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.