Importance-driven texture encoding based on samples

In this paper, we present an importance-driven texture encoding algorithm based on samples. Our algorithm determines a set of samples from source texture based on combined criteria which include compression ratio, visual attention and parameterization distortion. The sample set is used to encode the majority parts of the texture. The remaining regions are then encoded by traditional compression algorithm such as vector quantization. Our method can preserve details of important areas and be extended to dynamic textures. The decoding procedure is performed entirely in programmable graphics hardware, yielding real-time frame rates. Experimental results demonstrate the efficiency and performance of our algorithm.

[1]  Leonid Levkovich-Maslyuk,et al.  Texture compression with adaptive block partitions (poster session) , 2000, ACM Multimedia.

[2]  Michael Ashikhmin,et al.  Synthesizing natural textures , 2001, I3D '01.

[3]  Anton V. Pereberin.  Hierarchical Approach for Texture Compression , 2007 .

[4]  Brendt Wohlberg,et al.  A review of the fractal image coding literature , 1999, IEEE Trans. Image Process..

[5]  Marc Levoy,et al.  Fast texture synthesis using tree-structured vector quantization , 2000, SIGGRAPH.

[6]  Maneesh Agrawala,et al.  Rendering from compressed textures , 1996, SIGGRAPH.

[7]  KochChristof,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 1998 .

[8]  David Salesin,et al.  Image Analogies , 2001, SIGGRAPH.

[9]  Denis V. Ivanov,et al.  Color Distribution ‐ A New Approach to Texture Compression , 2000, Comput. Graph. Forum.

[10]  S Ullman,et al.  Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.

[11]  Pedro V. Sander,et al.  Texture mapping progressive meshes , 2001, SIGGRAPH.

[12]  Ming Hong Pi,et al.  Fast fractal image encoding based on adaptive search , 2001, IEEE Trans. Image Process..

[13]  Allen Gersho,et al.  Vector quantization and signal compression , 1991, The Kluwer international series in engineering and computer science.

[14]  Simon Fenney,et al.  Texture compression using low-frequency signal modulation , 2003, HWWS '03.

[15]  Y. Fisher.  Fractal image compression: theory and application , 1995 .