Image Compression Using Block Truncation Coding

Delp, Edward J.; Mitchell, O. Robert

doi:10.1109/tcom.1979.1094560

Cited by 753 publications

(265 citation statements)

References 6 publications

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“…In block truncation coding (BTC), the original image is divided into fixed-size non overlapping blocks of size M N [1] .The block size chosen is usually small to avoid the edge blurring and blocking effect. Each block is independently coded using a two level (1-bit) quantizer.…”

Section: Block Truncation Codingmentioning

confidence: 99%

A Comparative Study on Image Compression Using Half toning Based Block Truncation Coding for Color Image

M.S¹,

Priya²

2016

IJCI

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Section: Block Truncation Codingmentioning

confidence: 99%

A Comparative Study on Image Compression Using Half toning Based Block Truncation Coding for Color Image

M.S¹,

Priya²

2016

IJCI

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“…These include the vector quantization [1], fractal image compression [2], wavelet image compression [3], and block truncation coding (BTC) [4]. In this study, we focus on the last one.…”

Section: Introductionmentioning

confidence: 99%

“…Block truncation coding was originally proposed by Mitchel et al, in 1979 for grayscale image compression [4]. This technique divides an image into non-overlapping blocks, with each block encoded using a representative set of pixel values.…”

Section: Introductionmentioning

confidence: 99%

k-d Tree-Segmented Block Truncation Coding for Image Compression

Daga

Fernandez

2016

MATEC Web of Conferences

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Abstract. 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.

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“…Block Truncation Coding (BTC) [1], is a type of lossy image compression technique for grey scale images. It divides the original images into blocks and then uses a quantizer to reduce the number of grey levels.…”

Section: Introductionmentioning

confidence: 99%