A Block Based MAP Segmentation for Image Compression

1. A Block Based MAP Segmentation for Image Compression By Waseem Khatri

2. Motivation • Need for a Robust Segmentation method • Segmentation should yield larger homogeneous regions which are suitable for image compression • Object based image compression for better compression efficiency

3. Objective • Segmentation with Spatial Homogeneity – as it yields accurate segmentation results • Encoding and Decoding segmented objects independently – for better motion compensation

4. Drawback of Object Based Segmentation • Irregular shape of objects needs a significant number of bits for encoding • Many computation for object segmentation • Not suitable for real-time video coding applications Solution ? • Block – based object segmentation

5. MAP using GRF • To identify edges, monotone and textured blocks • Clique potentials for the GRF – guarantee the edge connectivity and the smoothness of the homogeneous block

6. Structure of Proposed Segmentation • Object based image compression encoder Partition Definition Partition Coding Texture Coding Block Classification/ Segmentation Numbering to Connected Homogeneous Blocks Assignment of Uncertainty Blocks

7. Block Classification/ Segmentation Numbering to Connected Homogeneous Blocks Assignment of Uncertainty Blocks • Block Classification – Determines whether the given image block belongs to a homogeneous block( textured or monotone) or an edge block • Numbering Homogenous blocks - Region Number is assigned to connected homogenous blocks • Uncertainty blocks – Edge blocks and Unnumbered isolated homogeneous blocks • Region Growing Method – Assigns uncertainty blocks to one of its neighboring homogeneous regions

8. MAP Criterion Let X denote a 2D random field representing a set of all possible characteristics where represents the set of all block indexes. means that the grey level distribution in the block at t is close to an edge block. implies a monotone block indicates a textured block

9. MAP Criterion Let Y denote a random field for the set of all observed gray levels in The optimal block label realization that maximizes the a posteriori probability is given by Where i.e. the gray level configuration of all blocks are independent Using GRF

10. Cliques

11. Numbering of Connected Homogeneous Blocks • Implemented using the following paper, L.Vincent, “Watershed in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Trans. Pattern Anal. Machine Intell., vol. 13, pp. 583-589, June 1991.

12. Assignment of Uncertainty Blocks • Uncertainty blocks are assigned to one of their neighboring numbered homogeneous regions Process : • Assign an uncertainty block to one of its neighboring regions if it yields the smallest difference between the block mean and that of the neighboring region to be assigned • Repeat for all uncertainty blocks until no block is left – i.e. we obtain a final block based image segmentation.

13. Expected Results

14. Expected Results

15. Conclusion • More accurate segmentation by reducing the block size • Proposed method can separate the contour blocks from the homogeneous region blocks • Since the contour of an object is represented in terms of blocks rather than pixels, we can change the balance between reconstructed video quality and the coding cost by adjusting the block size. • Textured region can be identified by considering the statistical distribution of the gray levels in the block into the segmentation criterion.

16. Thank-you !