Fast texture synthesis using tree-structured vector quantization
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
I3D '01 Proceedings of the 2001 symposium on Interactive 3D graphics
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Graphcut textures: image and video synthesis using graph cuts
ACM SIGGRAPH 2003 Papers
Texture optimization for example-based synthesis
ACM SIGGRAPH 2005 Papers
A lattice-based mrf model for dynamic near-regular texture tracking and manipulation
A lattice-based mrf model for dynamic near-regular texture tracking and manipulation
Generalized Fourier Descriptors with Applications to Objects Recognition in SVM Context
Journal of Mathematical Imaging and Vision
Block-based image compression with parameter-assistant inpainting
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Combined morphological-spectral unsupervised image segmentation
IEEE Transactions on Image Processing
A texture replacement method at the encoder for bit-rate reduction of compressed video
IEEE Transactions on Circuits and Systems for Video Technology
Overview of the H.264/AVC video coding standard
IEEE Transactions on Circuits and Systems for Video Technology
Image Compression With Edge-Based Inpainting
IEEE Transactions on Circuits and Systems for Video Technology
Hi-index | 0.00 |
In this paper, a content-based approach for video compression is proposed. The main novelty relies on the complete texture analysis/synthesis framework, which enables the use of multiple algorithms, depending on texture characteristics. The idea comes from the efficient MPEG prediction based on a best mode selection. Existing synthesis algorithms cannot be efficient in synthesizing every kind of texture but a certain range of them. 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: motion compensation or texture 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 in order to ensure 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. 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. The framework has been validated within an H.264/MPEG4-AVC video codec. Experimental results show significant bit-rate saving at similar visual quality levels, assessed using subjective tests. The method can be coupled with the future HEVC in which blocks can be skipped by the encoder to be synthesized at decoder side.