Still image compression with mean-residual domain adaptive resolution vector quantization technique

Takahiro Nakayama; Masahiro Konda; Koji Takeuchi; Koji Kotani; Tadahiro Ohmi

Still image compression with mean-residual domain adaptive resolution vector quantization technique

Takahiro Nakayama, Masahiro Konda, Koji Takeuchi, Koji Kotani, Tadahiro Ohmi

ENG - Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A novel image compression algorithm based on vector quantization (VQ) technique is proposed in this paper. Mean-residual domain adaptive resolution VQ method (AR-MVQ), which is composed of three key techniques, i.e., the resolution conversion, the block size alteration, and mean residual domain vector quantization (MVQ) can realize much superior compression performance than the worldwide standard JPEG and JPE02000. On the compression of the XGA (1024×768 pixels) natural images, AR-MVQ has realized a performance that is equal to the JPEG and the JPEG2000, Especially, the compression of the images including text exists an overwhelming performance superiority of 5 to 40 dB in compressed image quality.

Original language	English
Title of host publication	Image Processing, Biomedicine, Multimedia, Financial Engineering and Manufacturing - International Forum on Multimedia Image Processing, IFMIP - Proceedings of the Sixth Biannual World Automation Cong
Editors	M. Jamshidi, Y. Hata, A. Kamrani, J.S. Jamshidi
Pages	223-228
Number of pages	6
Publication status	Published - 2004 Dec 1
Event	Image Processing, Biomedicine, Multimedia, Financial Engineering and Manufacturing - International Forum on Multimedia Image Processing, IFMIP - Sixth Biannual World Automation Congress, WAC 2004 - Seville, Spain Duration: 2004 Jun 26 → 2004 Jul 1

Publication series

Name	Image Processing, Biomedicine, Multimedia, Financial Engineering and Manufacturing - Proceedings of the Sixth Biannual World Automation Congress

Other

Other	Image Processing, Biomedicine, Multimedia, Financial Engineering and Manufacturing - International Forum on Multimedia Image Processing, IFMIP - Sixth Biannual World Automation Congress, WAC 2004
Country	Spain
City	Seville
Period	04/6/26 → 04/7/1

Keywords

Adaptive resolution conversion
Codebook design
Mean-residual domain
Still image compression
Vector quantization

ASJC Scopus subject areas

Engineering(all)

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Cite this

Nakayama, T., Konda, M., Takeuchi, K., Kotani, K., & Ohmi, T. (2004). Still image compression with mean-residual domain adaptive resolution vector quantization technique. In M. Jamshidi, Y. Hata, A. Kamrani, & J. S. Jamshidi (Eds.), Image Processing, Biomedicine, Multimedia, Financial Engineering and Manufacturing - International Forum on Multimedia Image Processing, IFMIP - Proceedings of the Sixth Biannual World Automation Cong (pp. 223-228). (Image Processing, Biomedicine, Multimedia, Financial Engineering and Manufacturing - Proceedings of the Sixth Biannual World Automation Congress).

Still image compression with mean-residual domain adaptive resolution vector quantization technique. / Nakayama, Takahiro; Konda, Masahiro; Takeuchi, Koji; Kotani, Koji; Ohmi, Tadahiro.

Image Processing, Biomedicine, Multimedia, Financial Engineering and Manufacturing - International Forum on Multimedia Image Processing, IFMIP - Proceedings of the Sixth Biannual World Automation Cong. ed. / M. Jamshidi; Y. Hata; A. Kamrani; J.S. Jamshidi. 2004. p. 223-228 (Image Processing, Biomedicine, Multimedia, Financial Engineering and Manufacturing - Proceedings of the Sixth Biannual World Automation Congress).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nakayama, T, Konda, M, Takeuchi, K, Kotani, K & Ohmi, T 2004, Still image compression with mean-residual domain adaptive resolution vector quantization technique. in M Jamshidi, Y Hata, A Kamrani & JS Jamshidi (eds), Image Processing, Biomedicine, Multimedia, Financial Engineering and Manufacturing - International Forum on Multimedia Image Processing, IFMIP - Proceedings of the Sixth Biannual World Automation Cong. Image Processing, Biomedicine, Multimedia, Financial Engineering and Manufacturing - Proceedings of the Sixth Biannual World Automation Congress, pp. 223-228, Image Processing, Biomedicine, Multimedia, Financial Engineering and Manufacturing - International Forum on Multimedia Image Processing, IFMIP - Sixth Biannual World Automation Congress, WAC 2004, Seville, Spain, 04/6/26.

Nakayama T, Konda M, Takeuchi K, Kotani K, Ohmi T. Still image compression with mean-residual domain adaptive resolution vector quantization technique. In Jamshidi M, Hata Y, Kamrani A, Jamshidi JS, editors, Image Processing, Biomedicine, Multimedia, Financial Engineering and Manufacturing - International Forum on Multimedia Image Processing, IFMIP - Proceedings of the Sixth Biannual World Automation Cong. 2004. p. 223-228. (Image Processing, Biomedicine, Multimedia, Financial Engineering and Manufacturing - Proceedings of the Sixth Biannual World Automation Congress).

Nakayama, Takahiro ; Konda, Masahiro ; Takeuchi, Koji ; Kotani, Koji ; Ohmi, Tadahiro. / Still image compression with mean-residual domain adaptive resolution vector quantization technique. Image Processing, Biomedicine, Multimedia, Financial Engineering and Manufacturing - International Forum on Multimedia Image Processing, IFMIP - Proceedings of the Sixth Biannual World Automation Cong. editor / M. Jamshidi ; Y. Hata ; A. Kamrani ; J.S. Jamshidi. 2004. pp. 223-228 (Image Processing, Biomedicine, Multimedia, Financial Engineering and Manufacturing - Proceedings of the Sixth Biannual World Automation Congress).

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N2 - A novel image compression algorithm based on vector quantization (VQ) technique is proposed in this paper. Mean-residual domain adaptive resolution VQ method (AR-MVQ), which is composed of three key techniques, i.e., the resolution conversion, the block size alteration, and mean residual domain vector quantization (MVQ) can realize much superior compression performance than the worldwide standard JPEG and JPE02000. On the compression of the XGA (1024×768 pixels) natural images, AR-MVQ has realized a performance that is equal to the JPEG and the JPEG2000, Especially, the compression of the images including text exists an overwhelming performance superiority of 5 to 40 dB in compressed image quality.

AB - A novel image compression algorithm based on vector quantization (VQ) technique is proposed in this paper. Mean-residual domain adaptive resolution VQ method (AR-MVQ), which is composed of three key techniques, i.e., the resolution conversion, the block size alteration, and mean residual domain vector quantization (MVQ) can realize much superior compression performance than the worldwide standard JPEG and JPE02000. On the compression of the XGA (1024×768 pixels) natural images, AR-MVQ has realized a performance that is equal to the JPEG and the JPEG2000, Especially, the compression of the images including text exists an overwhelming performance superiority of 5 to 40 dB in compressed image quality.

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