Pixel clustering is a technique of content-adaptive data embedding in the area of high-performance reversible data hiding (RDH). Using pixel clustering, the pixels in a cover image can be classified into different groups based on a single factor, which is usually the local complexity. Since finer pixel clustering seems to improve the embedding performance, in this manuscript, we propose using two factors ...

In the area of reversible data hiding (RDH), one of the most popular techniques is prediction-error expansion (PEE), which hides data in the prediction errors with well-preserved image fidelity. The key to a successful PEE-based RDH implementation usually lies in prediction algorithms with high accuracy. Existing PEE-based RDH works often employ one single prediction algorithm, which is usually globally ...

In the area of reversible data hiding (RDH), multiple-histograms modification (MHM) has been widely recognized as one of the most high-performance techniques. With MHM, the correlation between the prediction-error (PE) and the local complexity (LC) can be well exploited for pixel sorting based data embedding, which is very important in MHM-based RDH algorithms for the obtained high image quality and ...

Devices equipped with algorithms for detecting infant cries allow parents to respond immediately to their crying babies. However, improving detection performance often requires complex algorithms that consume more computing resources, leading to increased power consumption and prices. In this study, we propose a novel approach that leverages first-order Wavelet Scattering Coefficients (WSC) as translation-...

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 flow ...

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