Lossy Image Compression Analysis using JPEG Algorithm on Rice Leaf Disease Dataset

Wahyu Saptha Negoro; Ratih  Adinda Destari; Asbon  Hendra Azhar; Fhery  Agustin

doi:10.35134/komtekinfo.v13i2.697

Authors

Wahyu Saptha Negoro Universitas Potensi Utama
Ratih Adinda Destari Universitas Potensi Utama
Asbon Hendra Azhar Universitas Potensi Utama
Fhery Agustin Universitas Potensi Utama

DOI:

https://doi.org/10.35134/komtekinfo.v13i2.697

Keywords:

JPEG, Lossy Compression, Rice Leaf Disease, PSNR, MSE, Compression Ratio

Abstract

The development of digital image processing technology has increased the need for more efficient image data storage and transmission, especially in the field of smart agriculture that utilizes digital images as a source of information. Rice leaf disease image datasets generally have quite large file sizes, requiring compression techniques to save storage capacity and speed up the data exchange process. This study aims to analyze the performance of lossy image compression using the JPEG algorithm on rice leaf disease datasets by evaluating file size efficiency and the quality of the compressed images. The research process includes collecting rice leaf disease image datasets, applying JPEG compression at several quality levels (quality factors), and measuring performance using the Compression Ratio (CR), Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE) parameters. The analysis results show that the lower the quality factor value used, the greater the compression ratio obtained, but the visual quality of the image decreases as indicated by the increase in the MSE value and the decrease in the PSNR value. Conversely, a higher quality factor is able to maintain image quality with the consequence of a larger file size. The findings of this study indicate that the JPEG algorithm is able to provide a good compromise between storage efficiency and visual image quality so that it remains suitable for use in rice leaf disease datasets, especially as a preprocessing stage in image processing and artificial intelligence systems for plant disease classification.

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