An efficient 2&amp;#x00D7;2 Tchebichef moments for mobile image compression

Ernawan, Ferda; Noersasongko, Edi; Abu, Nur Azman

doi:10.1109/ispacs.2011.6146066

Cited by 20 publications

(14 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, to the best of our knowledge, literature archives only one fast algorithm for the 8-point DTT, which requires a significant number of arithmetic operations [6]. Such high arithmetic complexity may be a hindrance for the adoption of the DTT in contemporary devices that demand low-complexity circuitry and low power consumption [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Discrete Tchebichef Transform Approximation for Image and Video Coding

Oliveira

Cintra

Bayer

et al. 2015

IEEE Signal Process. Lett.

View full text Add to dashboard Cite

In this paper, we introduce a low-complexity approximation for the discrete Tchebichef transform (DTT). The proposed forward and inverse transforms are multiplication-free and require a reduced number of additions and bit-shifting operations. Numerical compression simulations demonstrate the efficiency of the proposed transform for image and video coding. Furthermore, Xilinx Virtex-6 FPGA based hardware realization shows 44.9% reduction in dynamic power consumption and 64.7% lower area when compared to the literature.

show abstract

Section: Introductionmentioning

confidence: 99%

A Discrete Tchebichef Transform Approximation for Image and Video Coding

Oliveira

Cintra

Bayer

et al. 2015

IEEE Signal Process. Lett.

View full text Add to dashboard Cite

show abstract

“…An additional advantage, Tchebichef moment requires the evaluation of algebraic expression only. TMT has been widely used in many image processing applications such as image analysis [3], texture segmentation, multispectral texture, template matching, pose estimation, image reconstruction [4], image dithering [5][6], image projection [7] and image compression [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

TMT quantization table generation based on psychovisual threshold for image compression

Ernawan¹,

Abu

Suryana

2013

2013 International Conference of Information and Communication Technology (ICoICT)

Self Cite

View full text Add to dashboard Cite

The human visual systems are able to perceive various intensity of the colour image while it is not able to respond small different colour signals of the image. The sensitivity of the color image can be measured by a psychovisual threshold. A psychovisual threshold represents the sensitivity of the human eye to the image. The quantization tables are obtained to determine psychovisual threshold that can be perceived visually by the human eye. This paper presents a generating of the quantization tables from a psychovisual threshold on grayscale TMT image compression. This paper introduces the concept of psychovisual threshold into TMT image compression. TMT image compression has been shown to perform better than the standard JPEG image compression. This model has been implemented on TMT image compression. The experiment results show that a psychovisual threshold for TMT basis function provides better image compression performance.

show abstract

“…With the orthogonality, it is an efficient image descriptor because it can be used to represent an image without information redundancy in the moment set, and is able to detect any small variation in the pixel intensity. With its ability to effectively extract statistical and structural information of an image, TM has been widely used in various applications [18], [19], [20], [21]. The image information obtained from the TMs is global because the moments are computed over every image pixel.…”

Section: Introductionmentioning

confidence: 99%

Plain, edge, texture (PET) block classifier using Tchebichef moments and SVM

Lim

Thung

et al. 2013

2013 International Symposium on Intelligent Signal Processing and Communication Systems

View full text Add to dashboard Cite

This paper presents an image block classification method using Tchebichef moments (TMs) and support vector machine (SVM). The test images are divided into non-overlapping 16 × 16 blocks and transformed into moment domain using Discrete Tchebichef Transform. These moment features are then used in the image content (block) classification. SVM is used for learning and classifying the blocks into three types: "plain", "edge" and "texture", based on their moment energy level. Experimental results show that the proposed method works well and the classification accuracy is 98.7%.

show abstract

An efficient 2×2 Tchebichef moments for mobile image compression

Cited by 20 publications

References 11 publications

A Discrete Tchebichef Transform Approximation for Image and Video Coding

A Discrete Tchebichef Transform Approximation for Image and Video Coding

TMT quantization table generation based on psychovisual threshold for image compression

Plain, edge, texture (PET) block classifier using Tchebichef moments and SVM

Contact Info

Product

Resources

About