Unequal error protected ROI image transmission based on vector quantization [region of interest compression scheme]

2004 ◽  
Author(s):  
A. Ebrahimi-Moghadam ◽  
S. Shirani
Keyword(s):  
Vector Quantization ◽  
Region Of Interest ◽  
Image Transmission ◽  
Compression Scheme

A fast ELM-based machine compression scheme for underwater image transmission on a low-bandwidth acoustic channel

Sensor Review ◽  
2019 ◽  
Vol 39 (4) ◽  
pp. 542-553
Author(s):  
Shujing Zhang ◽  
Manyu Zhang ◽  
Yujie Cui ◽  
Xingyue Liu ◽  
Bo He ◽  
...  
Keyword(s):  
Image Compression ◽  
Principal Component ◽  
Image Transmission ◽  
Color Images ◽  
Compression Algorithm ◽  
Divide And Conquer ◽  
Perceptual Quality ◽  
Compression Scheme ◽  
Content Type ◽  
Processing Module

Purpose This paper aims to propose a fast machine compression scheme, which can solve the problem of low-bandwidth transmission for underwater images. Design/methodology/approach This fast machine compression scheme mainly consists of three stages. Firstly, raw images are fed into the image pre-processing module, which is specially designed for underwater color images. Secondly, a divide-and-conquer (D&C) image compression framework is developed to divide the problem of image compression into a manageable size. And extreme learning machine (ELM) is introduced to substitute for principal component analysis (PCA), which is a traditional transform-based lossy compression algorithm. The execution time of ELM is very short, thus the authors can compress the images at a much faster speed. Finally, underwater color images can be recovered from the compressed images. Findings Experiment results show that the proposed scheme can not only compress the images at a much faster speed but also maintain the acceptable perceptual quality of reconstructed images. Originality/value This paper proposes a fast machine compression scheme, which combines the traditional PCA compression algorithm with the ELM algorithm. Moreover, a pre-processing module and a D&C image compression framework are specially designed for underwater images.

scholarly journals OPTIMASI ALGORITMA LEARNING VECTOR QUANTIZATION (LVQ) DALAM PENGKLASIFIKASIAN CITRA DAGING SAPI DAN DAGING BABI BERBASIS GLCM DAN HSV

2018 ◽  
Vol 9 (1) ◽  
pp. 1-10
Author(s):  
Usman Sudibyo ◽  
Desi Purwanti Kusumaningrum ◽  
Eko Hari Rachmawanto ◽  
Christy Atika Sari
Keyword(s):  
Vector Quantization ◽  
Region Of Interest ◽  
Learning Vector Quantization ◽  
Gray Level ◽  
Minimum Error ◽  
Occurrence Matrix

Meningkatnya kebutuhan daging sapi, berdampak pada harga daging sapi. Harga daging sapi yang terus menerus mengalami kenaikan, tentunya menyebabkan penurunan penjualan daging sapi. Untuk mengantisipasi hal tersebut, maka beberapa pedagang mencampurkan daging sapi dengan daging babi. Dipilihnya daging babi, karena harga daging babi lebih murah dan warna serta tekstur daging babi yang mirip dengan daging sapi. Secara kasat mata daging sapi dan daging babi sulit untuk dibedakan bagi orang awam. Oleh karena itu, perlu adanya sistem yang dapat membedakan kedua daging. Penelitian ini menggunakan metode klasifikasi untuk membedakan kedua daging. Metode klasifikasi menggunakan algoritma Learning Vector Quantization. Dan penelitian ini memiliki tiga tahapan utama seperti preprocessing, segmentasi warna, ekstraksi fitur, dan klasifikasi. Preprocessing digunakan untuk mendapatkan Region of Interest (ROI) dengan memotong citra dan mengubah ukuran citra. Segmentasi warna menggunakan metode HSV untuk mendapatkan kedalaman warna citra dan ekstraksi fitur mengguakan Gray Level Co-occurrence Matrix (GLCM) untuk mendapatkan fitur dari kontras, korelasi, energi, dan homogenitas. Hasil klasifikasi dengan algoritma LVQ mendapatkan akurasi tertinggi 76,25%. Algoritma telah diuji dengan MSE untuk mengetahui minimum error dan PSNR digunakan sebagai pengukuran kualitas citra pengolahan.

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