Summative Stereoscopic Image Compression using Arithmetic Coding

Poolakkachalil, Thafseela Koya; Chandran, Saravanan

doi:10.52549/ijeei.v7i3.755

Cited by 2 publications

(1 citation statement)

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“…Perceptual quality (as estimated with the MS-SSIM index) exhibited substantial improvement across all test images and bit rates. Poolakkachalil and Chandran (2019) discussed the use of arithmetic coding for stereoscopic image compression. The proposed method exploited the spatial redundancy that occurs among the two-image pair to achieve a greater compression ratio in comparison to the individual compression of each frame.…”

Section: Related Work On Image Compressionmentioning

confidence: 99%

Application of Principal Component Analysis to advancing digital phenotyping of plant disease in the context of limited memory for training data storage

2023

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Despite its widespread employment as a highly efficient dimensionality reduction technique, limited research has been carried out on the advantage of Principal Component Analysis (PCA)–based compression/reconstruction of image data to machine learning-based image classification performance and storage space optimization. To address this limitation, we designed a study in which we compared the performances of two Convolutional Neural Network-Random Forest Algorithm (CNN-RF) guava leaf image classification models developed using training data from a number of original guava leaf images contained in a predefined amount of storage space (on the one hand), and a number of PCA compressed/reconstructed guava leaf images contained in the same amount of storage space (on the other hand), on the basis of four criteria – Accuracy, F1-Score, Phi Coefficient and the Fowlkes–Mallows index. Our approach achieved a 1:100 image compression ratio (99.00% image compression) which was comparatively much better than previous results achieved using other algorithms like arithmetic coding (1:1.50), wavelet transform (90.00% image compression), and a combination of three transform-based techniques – Discrete Fourier (DFT), Discrete Wavelet (DWT) and Discrete Cosine (DCT) (1:22.50). From a subjective visual quality perspective, the PCA compressed/reconstructed guava leaf images presented almost no loss of image detail. Finally, the CNN-RF model developed using PCA compressed/reconstructed guava leaf images outperformed the CNN-RF model developed using original guava leaf images by 0.10% accuracy increase, 0.10 F1-Score increase, 0.18 Phi Coefficient increase and 0.09 Fowlkes–Mallows increase.

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Section: Related Work On Image Compressionmentioning

confidence: 99%