Efficient chain-code encoding for segmentation-based image compression

Abstract

This paper presents a new and eflicient method of encoding uniform image @m and lines. Regions and lines are obtained as the result of image segmentation. split and merge image compression. or as the output of line and polygon drawing algorithms. Lines and contours of uniform regions, are encoded using chain-code. The chaincode is obtained in a way that is &cient with respect to bit-rate and produces lossless contour and line encoding. A lossy method for contour etlcoding is also presented.A set of experiments to compare the performame of traditional chain-code contour etlcoding with the improved contour encoding is presented. The results show a reduction of about 50% in the bit-rate with no reconstruction mor. Efficient lossless chain-encoding of region boundariesSimple modi6cations to t k enoodiog cm lead to a significant saving in bit rate. Pavlidis2 proposes a relative chaincode with variable code per direction. The code used in this paper is a fixed length relative code with priority where a lower number denom higher priority. This coding methcd is designed to inaease code redundancy.A cut of almost 50% in the chaincode can be achieved by modifying the traditional approach of contour tracing into a region boundary tracing method. The problem with cantour following is that pixels 011 region contours are also on the boundaries between regions. Hence, the contour following algarithm etlcodes each boundary twice. The change in approach entails that chains does not descibe closed objects. Therefon?. a few adjustment to the encoding scheme are required. First. one cannot use the condition that the ~d i u a t e s ofthe initial pixel and the coordinates of the terminating pixel of achain are the same, as the means to identify the terminaticm ofachain Rather. achain is ended with a relative prioritized code denoting going back to the previous chain element. Second. this approech has an additional overhead due to the r q u h n e n t to include the coordinates of the initial pixel. Now. the same region may be represented by more than one chain and hence more than om initial pixel. Another modification is that instead of attaching the information about a region to it chainade, the information about all the regions in the image proceeds or succeeds the chaincodes of the regions.The coding scheme bas a built-in LZW Compressa~ In a fork poinl. this algorithm checks the compression table. If possible it chooses a route that is an entry in the table in favor of a mute that re@m updating the table. A lossy option that produce smooth edges is also available Experiments and resultsThe different improvements described in this paper has been applied to four images. One of these images has been obtained from line and polygon drawing. Other images are "natural" iinages that have been passed thmngh segmentation. Our findings an?: 1) Unix compression of the regular chain& achieves between 1.5 times compression to 10 times compression. 2) Boundary Tracing with lossless unnpssion is better than contour tracing with lossless compression...

Image Compression with Modified Skipline Encoding and Curve Fitting

“…Huffman prefix codes and arithmetic codes are applied after the processing in order to obtain the final compressed code stream. E. Tamir et al proposed an efficient Chain Code encoding for segmentation based image compression [11]. In this, regions and lines are obtained as results of image segmentation, split and merge image compression or as the output of line and polygon algorithm.…”

“…A good number of image compression methods has been proposed by different researchers based on techniques like dividing image into blocks [1,2,3,4,5,6,7], compressions based on edge detection [8,9], chain coding [10,11], skip line encoding [12,13] etc. These methods exploit the spatial redundancy or correlation between pixels in the image.…”

Image expansion using segmentation-based method

“…Using more complicated and more accurate edge coding technique is one of the suggestions for future study. There are many techniques for edge coding that can be used to improve the quality of the expanded edges [83,84,85].…”