An adaptive large DCT psychovisual threshold in image compression

Ernawan, Ferda and M. Nomani, Kabir and Zuriani, Mustaffa and Ramalingam, Mritha (2019) An adaptive large DCT psychovisual threshold in image compression. In: IEEE International Conference on Knowledge Innovation and Invention 2019 (IEEE ICKII 2019), 13 - 16 Julai 2019 , Seoul, South Korea. pp. 1-5.. (Unpublished)

[img] Pdf
20. An improved image compression technique using large.pdf
Restricted to Repository staff only

Download (532kB) | Request a copy
20.1 An improved image compression technique using large.pdf

Download (90kB) | Preview


Nowadays, multimedia communication requires high bandwidth and data transfer rate to transfer multimedia data. Image compression is one of alternative solutions to reduce the storage and transmission. An adaptive image compression technique has been widely used in many applications, it can be done by customizing quantization tables based on user preference. A scaling factor is one of scaling techniques for customizing the quantization values uniformly. Consequently, a scaling quantization table uniformly can significantly effect to the error reconstruction and compression rate. This paper proposes an adaptive large psychovisual threshold for customizing large quantization tables in image compression. The proposed adaptive large psychovisual threshold is designed based on a smooth curve of the absolute reconstruction error by incrementing the DCT coefficients one at a time for each frequency order. The experimental results show that the performance of adaptive large DCT psychovisual threshold achieves high image quality and minimum average bit length of Huffman code. The visual image of the proposed method also clearly shows that it does not appear boundary effect when the reconstructed image was zoomed in to 400%.

Item Type: Conference or Workshop Item (Lecture)
Uncontrolled Keywords: Psychovisual threshold; DCT; Image compression; Reconstruction
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Pn. Hazlinda Abd Rahman
Date Deposited: 13 Dec 2019 08:30
Last Modified: 13 Dec 2019 08:30
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item