MATEC Web of Conferences
Volume 56, 20162016 8th International Conference on Computer and Automation Engineering (ICCAE 2016)
|Number of page(s)||5|
|Section||Image Processing and Application|
|Published online||26 April 2016|
k-d Tree-Segmented Block Truncation Coding for Image Compression
1 Ateneo de Manila University, Loyola Heights, Katipunan Ave, Quezon City, Philippines
2 University of the Philippines Visayas Tacloban College, Tacloban City, Leyte, Philippines
Block truncation coding (BTC) is a class of image compression algorithms whose main technique is the partitioning of an image into pixel blocks that are then each encoded using a representative set of pixel values. It is commonly used because of its simplicity and low computational complexity. The Quadtree-segmented BTC (QTS-BTC), which utilizes a dynamic hierarchical segmentation technique, is among the most efficient in the BTC class. In this study, we propose a new BTC variant that introduces two ideas: (1) the use of a k-d tree for segmentation and (2) the use of a Mean Squared Error (MSE) threshold for dynamically determining the granularity of the blocks. We refer to this new BTC variant as the k-d Tree Segmented BTC (KTS-BTC), and we test this against some of the existing BTC variants by running the algorithms on a standard image compression dataset. The results show that the proposed variant yields low bit rates of the compressed images, even outperforming the state-of-the-art QTS-BTC, without a significant reduction in image quality as measured using the Peak Signal-to-Noise Ratio (PSNR). The utilization of k-d tree for image segmentation is further shown to have more impact than that of employing the MSE thresholding scheme as a block activity classifier.
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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