This thesis comes within the scope of new generation video compression schemes. In particular, it aims at improving the coding efficiency for textured regions in images and videos. At low bit-rate, textures are degraded, resulting in visually annoying flat tints. The basic assumption is based on the human visual system properties, which prefer synthesized details to flat color, even if the output surface is not exactly the source texture. In this work, texture synthesis algorithms from the literature are adapted in order to fit the coding context: filling textures which are not entirely transmitted. This approach is designed to be jointly used with current and future standard compression schemes. At encoder side, texture analysis includes segmentation and characterization tools, in order to localize candidate regions for synthesis. The corresponding areas are not encoded. The decoder fills them using texture synthesis. The remaining regions in images are classically encoded. They can potentially serve as input for texture synthesis. The chosen tools are developed and adapted with an eye to ensuring the coherency of the whole scheme. Thus, a texture characterization step provides required parameters to the texture synthesizer. Two texture synthesizers, including a pixel-based and a patch-based approach, are used on different types of texture, complementing each other. A first scheme is proposed for intra frame coding. Then, a temporal method is developed. The scheme is coupled with a motion estimator in order to segment coherent regions and to interpolate rigid motions using an affine model. Inter frame adapted synthesis is therefore used for non-rigid texture regions. Assessing the quality of decoded frames by such schemes, using objective methods, is problematic. Results on bit-rate savings are presented with the assumption of similar visual quality. Thus, now subjective tests provide for now the assessment. At comparable visual quality, up to 33% bit-rate is preserved, compared to H.264/AVC, on many SD and CIF sequences.
