A new model for color image compression using modified hierarchical prediction

In today´s life, images play a significant role in many application fields for numerous purposes. Image processing has to face the huge challenges because of images created in digital format which leads to huge data volumes. In recent days, to meet the diverse type of real time applications, the Joint Photographic Experts Group (JPEG) compression techniques are used, which are used to minimize the expenditure of possessions such as hard disk space and transmission bandwidth. Preceding paper utilized new lossless color image compression algorithm based on the hierarchical prediction and context-adaptive arithmetic coding. In this paper the specified image is transformed into YCuCv color space by RCT (Reversible Color Transform) coordination by using lossless compression technique and image compression is acquired. Preserving the sharpness of the original image is a prominent factor in image compression. New schemes are required in preserving the sharpness and reduction in bitrates in the further progress. Proposed paper recommended the approach which reduces the bitrates more than 1. The proposed scheme is named as Modified Hierarchical Prediction which removes the enormous prediction error rate near edges and preserves the sharpness of images. This paper considers the vertical, horizontal and diagonal (left up, left down and right up, right down) predictors to predict pixels. Diagonal predictor enhances the perdition accuracy of pixels in Hierarchical Prediction. The experimental result shows that the proposed Modified Hierarchical Prediction based scheme preserve the sharpness of the image.