一种基于编码单元和提示分割的H.265/HEVC细粒度ROI视频加密算法
A H.265/HEVC Fine-Grained ROI Video Encryption Algorithm Based on Coding Unit and Prompt Segmentation
AI总结：
本文提出基于编码单元和提示分割的细粒度ROI视频加密算法，通过改进的ROI映射方法和多语法元素选择加密方案，提升加密精度与安全性，适用于医疗、军事等敏感领域。
摘要：
ROI (Region of Interest) 视频选择性加密基于H.265/HEVC，通过扰动与目标区域相关的语法元素来保护视频敏感区域。然而，现有方法通常采用Tile（相对较大的尺寸）作为最小加密单元，导致加密区域不准确和加密精度低。为解决此问题，本文提出一种基于编码单元（CUs）和提示分割的细粒度ROI视频选择性加密算法。首先，为实现更精确的ROI获取，提出基于提示分割的新型ROI映射方法，将ROI精确映射到小$8\times8$ CU级别，显著提升加密区域的精度。其次，提出基于多语法元素的选择性加密方案，对高精度ROI内的语法元素进行扭曲，以有效保护ROI的安全性。最后，设计基于脉冲编码调制（PCM）模式和运动矢量（MV）限制的扩散隔离，通过PCM模式和MV限制策略对受影响的CU进行处理，以解决加密过程中的扩散问题。上述三种策略打破了现有ROI加密中使用Tile的固有机制，将ROI视频加密的细粒度水平推至最小$8\times8$ CU精度。实验结果表明，所提算法能够准确分割ROI区域，有效扰动这些区域内的像素，并消除加密引入的扩散伪影。该方法在医疗成像、军事监控和偏远地区等应用中展现出巨大潜力。
英文摘要：
ROI (Region of Interest) video selective encryption based on H.265/HEVC is a technology that protects the sensitive regions of videos by perturbing the syntax elements associated with target areas. However, existing methods typically adopt Tile (with a relatively large size) as the minimum encryption unit, which suffers from problems such as inaccurate encryption regions and low encryption precision. This low-precision encryption makes them difficult to apply in sensitive fields such as medicine, military, and remote sensing. In order to address the aforementioned problem, this paper proposes a fine-grained ROI video selective encryption algorithm based on Coding Units (CUs) and prompt segmentation. First, to achieve a more precise ROI acquisition, we present a novel ROI mapping approach based on prompt segmentation. This approach enables precise mapping of ROIs to small $8\times8$ CU levels, significantly enhancing the precision of encrypted regions. Second, we propose a selective encryption scheme based on multiple syntax elements, which distorts syntax elements within high-precision ROI to effectively safeguard ROI security. Finally, we design a diffusion isolation based on Pulse Code Modulation (PCM) mode and MV restriction, applying PCM mode and MV restriction strategy to the affected CU to address encryption diffusion during prediction. The above three strategies break the inherent mechanism of using Tiles in existing ROI encryption and push the fine-grained level of ROI video encryption to the minimum $8\times8$ CU precision. The experimental results demonstrate that the proposed algorithm can accurately segment ROI regions, effectively perturb pixels within these regions, and eliminate the diffusion artifacts introduced by encryption. The method exhibits great potential for application in medical imaging, military surveillance, and remote areas.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
