Original scientific paper
https://doi.org/10.32985/ijeces.14.5.4
JPEG2000-Based Semantic Image Compression using CNN
Anish Nagarsenker
; Dr Vishwanath Karad MIT World Peace University, School of Electronics and Communication Engineering Kothrud, Pune, India
Prasad Khandekar
; Dr Vishwanath Karad MIT World Peace University, School of Electronics and Communication Engineering Kothrud, Pune, India
Minal Deshmukh
; Vishwakarma Institute of Information and Technology, Department of Electronics and Telecommunication Engineering Pune, India
Abstract
Some of the computer vision applications such as understanding, recognition as well as image processing are some areas where AI techniques like convolutional neural network (CNN) have attained great success. AI techniques are not very frequently used in applications like image compression which are a part of low-level vision applications. Intensifying the visual quality of the lossy video/image compression has been a huge obstacle for a very long time. Image processing tasks and image recognition can be addressed with the application of deep learning CNNs as a result of the availability of large training datasets and the recent advances in computing power. This paper consists of a CNN-based novel compression framework comprising of Compact CNN (ComCNN) and Reconstruction CNN (RecCNN) where they are trained concurrently and ideally consolidated into a compression framework, along with MS-ROI (Multi Structure-Region of Interest) mapping which highlights the semiotically notable portions of the image. The framework attains a mean PSNR value of 32.9dB, achieving a gain of 3.52dB and attains mean SSIM value of 0.9262, achieving a gain of 0.0723dB over the other methods when compared using the 6 main test images. Experimental results in the proposed study validate that the architecture substantially surpasses image compression frameworks, that utilized deblocking or denoising post- processing techniques, classified utilizing Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measures (SSIM) with a mean PSNR, SSIM and Compression Ratio of 38.45, 0.9602 and 1.75x respectively for the 50 test images, thus obtaining state-of-art performance for Quality Factor (QF)=5.
Keywords
Computer Vision; Neural Networks; CNN; MSROI; Compression; JPEG2000;
Hrčak ID:
303569
URI
Publication date:
5.6.2023.
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