Segmentation-based image compression

In the presenting paper we are dealing with to develop a lossless image compression (IC) method to utilize spatial redundancies inbuilt in image data which employs a best possible amount of segmentation information. To obtaining Multiscale segmentation we are using a earlier proposed transform which gives a tree-structured segmentation of the picture into regions which are identified by grayscale homogeneity. In the given proposed algor we have to shorten the tree to controlling the size and no of regions so that we can get a rate balance between the derived the coding gain and the operating cost inherent in coding the segmented data. Another uniqueness of the given proposed approach is that we are using an image model contain individual descriptions of the pixels lying close to the edges of a section and others lying in the center. In our results we can see that this proposed algorithm is providing better performance comparable to all the best available methods and it provides 15-20% better compression if we compare it with the JPEG lossless image compression standard for a enormous variety of images.

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Segmentation-based image compression

Cited by 2 publications

References 4 publications

Image Compression Using Polynomial Fitting

Image Compression Using Polynomial Fitting

Comparison of Segmentation-Based Image Compression Using Threshold, Region Growing, and Edge Detection

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