Designing JPEG quantization tables based on human visual system
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Publisher
IEEE
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http://xplorestaging.ieee.org/ielx5/6632/17708/00822921.pdf?arnumber=822921
Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Low-complexity JPEG quantization table requiring only bit-shift operations;The Imaging Science Journal;2022-11-17
2. Image Fusion using Multi Resolution transforms with Human Visual System;Journal of Physics: Conference Series;2021-05-01
3. QNet: An Adaptive Quantization Table Generator Based on Convolutional Neural Network;IEEE Transactions on Image Processing;2020
4. Knowledge-based genetic algorithm approach to quantization table generation for the JPEG baseline algorithm;TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES;2016
5. A simple VLSI architecture for computation of 2D DCT, quantisation and zig-zag ordering for JPEG;International Journal of Signal and Imaging Systems Engineering;2012
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