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Turtle edge encoding and flood fill based image compression scheme

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Abstract

Over the last two decades, great improvements have been made in image and video compression techniques driven by a growing demand for storage and transmission of visual information. This paper focuses on image compression, the main objective of an image compression technique is to remove as much redundant information as possible without destroying the image integrity. This paper proposes an edge based image compression scheme for cartoon images. Initially the edges of the image are identified using zero-crossings edge detector, and the edges are decoded by using a novel encoder based on turtle graphics. From the edge map the closed regions are labelled to estimate the color quantization levels. However the isolated edges falls inside the closed regions are stored separately and the region is encoded with its color/gray value at a random seed pixel. While decoding the image, a flood-fill algorithm is used to fill each region by its corresponding color, starting from the seed point. The boundary of each region is marked with the edge contour (only for the closed regions), and the isolated edges are marked over the decoded image from the original edge map. The proposed Turtle Edge encoder and flood-fill based image compression approach is analyzed with a collection of cartoon images. The performance of the proposed compression method is compared with the state-of-art compression methods like JPEG and JPEG2000 and the recent algorithms, the experimental results indicate that the proposed turtle edge and flood-fill based approach is able to achieve better compression ratio within less computation time.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India

    Y. Arockia Raj

  2. Vellamal College of Engineering and Technology, Madurai, Tamil Nadu, India

    P. Alli

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  1. Y. Arockia Raj
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  2. P. Alli
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Arockia Raj, Y., Alli, P. Turtle edge encoding and flood fill based image compression scheme. Cluster Comput 22 (Suppl 1), 361–377 (2019). https://doi.org/10.1007/s10586-018-1994-5

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