Luminance-model-based DCT quantization for color image compression

Ahumada, Albert J.; Peterson, Heidi A.

doi:10.1117/12.135982

Cited by 259 publications

(143 citation statements)

References 6 publications

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“…The relationship between the two coefficients is altered so that they accomplish a certain necessity relying on the watermark bit itself [7]. For detection, the image is broken up into those same 8 x 8 blocks, and a DCT function is performed.…”

Section: 3 3 3 3 a An N A Ad Dd DI It Ti Io On Na Al L E Ex Xp Pe mentioning

confidence: 99%

Introduction

2018

Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/SIMULINK®

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A Ab bs st tr ra ac ct tThis is the Final Project Report as being composed of an extensive summary of activities and results made by the student Abdullah S. A. Alzaid while undertaking the Watermark Project. This project acquaints an algorithm of digital watermarking which is based on Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). In accord with the characters of human vision, the main objective of the project is to be focused on developing an image watermarking algorithm by taking advantage of both the DCT and DWT transforms and analysis of the algorithm on the basis of invisibility, distortion and robustness to attacks. The simulation results show that this algorithm is invisible and has good robustness for some common image processing operations. By the use of Matlab software, the two structures have been coded and then implemented properly.

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Section: 3 3 3 3 a An N A Ad Dd DI It Ti Io On Na Al L E Ex Xp Pe mentioning

confidence: 99%

Introduction

2018

Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/SIMULINK®

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“…where L min and L max are the minimum and maximum display luminances, M g is the total number of gray scale levels, and T is given by the following parabolic approximation [15]:…”

Section: Perceptual Contrast Sensitivity Threshold Model and Jnd Compmentioning

confidence: 99%

A no-reference objective image quality metric based on perceptually weighted local noise

Zhu

Karam

2014

J Image Video Proc

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This work proposes a perceptual based no-reference objective image quality metric by integrating perceptually weighted local noise into a probability summation model. Unlike existing objective metrics, the proposed no-reference metric is able to predict the relative amount of noise perceived in images with different content, without a reference. Results are reported on both the LIVE and TID2008 databases. The proposed no-reference metric achieves consistently a good performance across noise types and across databases as compared to many of the best very recent no-reference quality metrics. The proposed metric is able to predict with high accuracy the relative amount of perceived noise in images of different content.

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“…Methods for lossless image compression include Run-length encoding [12], Predictive Coding [13]- [14], Arithmetic coding [15], and Entropy encoding such as Huffman [16]- [19] coding. On the other hand, lossy compression techniques include Quantization [20]- [21], Transform coding including Discrete Cosine Transform [22]- [23], Karhunen-Lo`eve transform [24] and Wavelet Transforms [25]- [27]. This paper proposes a lossy compression technique incorporating Linear Prediction coding, Quantization and Huffman coding.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Lossy Predictive Huffman Coding

2017

IJAERD

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Luminance-model-based DCT quantization for color image compression

Cited by 259 publications

References 6 publications

Introduction

Introduction

A no-reference objective image quality metric based on perceptually weighted local noise

Hybrid Lossy Predictive Huffman Coding

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