One of the critical challenges facing 3D video systems and images such as holography lies in their compression technique. High-efficiency video coding (HEVC) has emerged as one of the leading schemes to address this challenge. In this article, a novel method based on wavelet transform is presented to improve HEVC, particularly in digital holography systems (object plane). In this regard, wavelet and resizing are included in the coding process, while extra HEVC decoders and encoders are added to predict and decrease errors in the target. Simulation results reveals that the proposed algorithm reduces Bjøntegaard-Delta (BD) bitrate 17.5% (based on average BD-Rate values) compared to the original HEVC (H.265) scheme while maintaining signal fidelity and even enhancing it slightly. We observe an increased BDpeak-signal-to-noise ratio (BD-PSNR) in real and imaginary parts of digital holograms of high rate quantization values up to 1.1 dB.
<div>This paper outlines a new approach to detect anomalies in hyperspectral images based on peripheral pixels. The proposed methodology contains two main steps. First, a new distance score is introduced based on the sigmoid function and root mean square error (RMSE). We estimate how likely the target pixel is an anomaly by averaging the new metric over its neighboring window.</div><div> Second, a state-of-the-art method is applied to eliminate unacceptable objects according to their size. In this light, the objects whose size is out of an acceptable interval are removed.</div><div> Comprehensive experimental evaluations have been conducted to confirm that the proposed method significantly outperforms several recent algorithms in accuracy and computational time.</div>
<div>This paper outlines a new approach to detect anomalies in hyperspectral images based on peripheral pixels. The proposed methodology contains two main steps. First, a new distance score is introduced based on the sigmoid function and root mean square error (RMSE). We estimate how likely the target pixel is an anomaly by averaging the new metric over its neighboring window.</div><div> Second, a state-of-the-art method is applied to eliminate unacceptable objects according to their size. In this light, the objects whose size is out of an acceptable interval are removed.</div><div> Comprehensive experimental evaluations have been conducted to confirm that the proposed method significantly outperforms several recent algorithms in accuracy and computational time.</div>
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