Secure Transmission of Compressed Medical Image Sequences on Communication Networks Using Motion Vector Watermarking
Source abstract
Abstract
Medical imaging plays a key role within modern hospital management systems for diagnostic purposes. Compression methodologies are extensively employed to mitigate storage demands and enhance transmission speed, all while upholding image quality. Moreover, an increasing number of hospitals are embracing cloud computing for patient data storage, necessitating meticulous scrutiny of server security and privacy protocols. Nevertheless, considering the widespread availability of multimedia tools, the preservation of digital data integrity surpasses the significance of compression alone. In response to this concern, we propose a secure storage and transmission solution for compressed medical image sequences, such as ultrasound images, utilizing a motion vector watermarking scheme. The watermark is generated employing an error-correcting code known as Bose-Chaudhuri-Hocquenghem (BCH) and is subsequently embedded into the compressed sequence via block-based motion vectors. In the process of watermark embedding, motion vectors are selected based on their magnitude and phase angle. When embedding watermarks, no specific spatial area, such as a region of interest (ROI), is used in the images. The embedding of watermark bits is dependent on motion vectors. Although reversible watermarking allows the restoration of the original image sequences, we use the irreversible watermarking method. The reason for this is that the use of reversible watermarks may impede the claims of ownership and legal rights. The restoration of original data or images may call into question ownership or other legal claims. The peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) serve as metrics for evaluating the watermarked image quality. Across all images, the PSNR value exceeds 46 dB, and the SSIM value exceeds 0.92. Experimental results substantiate the efficacy of the proposed technique in preserving data integrity.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Steganography and Watermarking Techniques · Chaos-based Image/Signal Encryption · Advanced Data Compression Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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