Absolute moment block truncated coding (AMBTC) is a lossy image compression technique aiming at low computational cost, and has been widely studied. Previous studies have investigated the performance improvement of AMBTC; however, they often over describe the details of image blocks during encoding, causing an increase in bitrate. In this paper, we propose an efficient method to improve the compression ...
Reversible data hiding (RDH) allows carrying secret information in cover media without introducing permanent distortion. For a RDH method, the important performance measurements are embedding capacity and image quality. Since embedding capacity is an important requirement in the field of data hiding, it is necessary to consider the security of data embedding in RDH applications. In general, RDH algorithms ...
In prediction error-based reversible data hiding, multiple histograms modification (MHM) is well known for high image quality and thus has received wide attention in recent years. However, the computational cost for performance optimization in MHM is too high, which is particularly critical for real-time applications. This manuscript aims to reduce the computational complexity of MHM by presenting ...
Blockchain is distributed, tamper proof,decentralized, and traceable, but it is difficult to implement. The introduction of smart contracts effectively solves this problem.This research uses the combination of privacy computing technology and blockchain technology to create an efficient and compliant solution for the data element market. With the help of smart contract technology based on blockchain,...
To solve the conflict of interests between citizens’ travel and public transportation enterprises, and alleviate the pressure of passenger flow at morning and evening peak bus stop, a multimodal combination optimization model for public transportation scheduling was proposed. Because the public transport enterprise adopted the conventional dispatching mode, there was a mismatch between passenger ...
This study addresses limitations in traditional Problem-Based Learning (PBL) for object-oriented programming (OOP) education, which focuses on isolated programming knowledge and fails to personalize learning. We propose an AI-driven framework where students collaborate with Agentic AI to tackle authentic challenges. The AI assists by decomposing projects into Minimum Viable Solutions (MVS), co-designing ...
