Abstract
The computer vision branch of the artificial intelligence field is concerned with developing algorithms for analyzing video image content. Extracting edge information, which is the essential process in most pictorial pattern recognition problems. A new method of edge detection technique has been introduces in this research, for detecting boundaries.
 Selection of typical lossy techniques for encoding edge video images are also discussed in this research. The concentration is devoted to discuss the Block-Truncation coding technique and Discrete Cosine Transform (DCT) coding technique. In order to reduce the volume of pictorial data which one may need to store or transmit, the research modifies a method for video image data compression based on the two-component code; in this coding technique, the video image is partitioned into regions of slowly varying intensity. The contours separating the regions are coded by DCT, while the rest image regions are coded by Block-Truncation Coding. this hybrid coding technique called segmented image coding (SIC). Also in this paper A modify of the four step search for motion Estimation technique was produce. for searching scheme has been introduced which is contributed in decreasing the motion estimation searching time of the successive inter frames.
Highlights
The demand for communications with moving video picture is rapidly increasing.Video is required in many remote video conferencing systems, and it is expected that in near future cellular telephone systems will send and receive real-time video
A typical system needs to send dozens of individual frames per second to create an illusion of a moving picture
The method significantly reduces the amount of computation required by most traditional coding methods such as transform coding, yet it still produces an acceptable quality of reproduction
Summary
The demand for communications with moving video picture is rapidly increasing. Video is required in many remote video conferencing systems, and it is expected that in near future cellular telephone systems will send and receive real-time video. The encoding process is adopted by used two compression image data techniques to encoded the same digital image, so we adopt the discrete cosine transform coding on edge region, while the block truncation method is used to encode the rest region. The DCT operation transforms a block of pixels into the set of DCT coefficients that represent the block in the spatial frequency domain This is itself does not give compression since the information is merely being represented in different form [1,2]. Step 2: Quantization process: by which the less significant DCT coefficients are wiped out The result of this lossy transformation is an integer produced by dividing each of the coefficients in the 8 x 8 DCT block by a weighted value taken from a specified table. Combine the two least probable source symbols: 1. Form a new source symbol with a probability equal to the sum of the probabilities of the two least probable symbols
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