An Adaptive Edge-Preserving Modified Block Truncation Coding Framework with Aes Encryption for Secure Educational Content Transmission

Abstract

With the emergence of online learning environments and digital learning platforms, the need for efficient compression and secure transmissions of educational content has become a greater challenge. Instructional images, diagrams, notes, and multimedia resources may be quite large and complex, and in many cases need to be compressed to minimize storage space and transmission rate, while maintaining clarity and fidelity to the original image. In this paper, an Adaptive Edge-Preserving Modified Block Truncation Coding (AEP-MBTC) Framework is proposed which combines Advanced Encryption Standard (AES) for secure transmission of educational content. The proposed scheme introduces an adaptive edge preserving scheme for analysing the local image features and dynamically controls the quantization process to avoid the traditional blocking artifacts problem of conventional Block Truncation Coding scheme and to retain important image edge information. A modified threshold selection mechanism is further provided to obtain reconstructed images of a higher compression ratio and of a better image quality. Once compression is done, the compressed image is encrypted to maintain confidentiality, integrity and resistance to unauthorized access during network transmission by using the AES encryption algorithm. Adaptive compression and cryptographic security combine for a two-layer system that is ideal for cloud-based learning management systems, e-learning platforms, digital libraries and remote education applications. The proposed framework is tested using typical educational image datasets by objective quality metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM), Computation ratio (CR) and computational execution time. Experimental results show that the proposed approach is robust and effective to reconstruct the image with better quality, edge preservation, compression efficiency and security than conventional BTC-based compression techniques. Moreover, the complexity of computation is kept low, enabling the real-time delivery of educational content over constrained networks. The proposed AEP-MBTC with AES system is efficient, secure, and scalable for providing a reliable educational image compression in smart learning environments along with a protected transmission of digital contents in modern smart learning environments.