High performance holographic video compression using spatio-temporal phase unwrapping

Gonzalez, Sorayda Trejos; Velez-Zea, Alejandro; Barrera-Ramírez, John Fredy

doi:10.1016/j.optlaseng.2024.108381

Optics and Lasers in Engineering

2024

DOI: 10.1016/j.optlaseng.2024.108381

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High performance holographic video compression using spatio-temporal phase unwrapping

Sorayda Trejos Gonzalez,

Alejandro Velez-Zea,

John Fredy Barrera-Ramírez

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Cited by 1 publication

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Syntax-Guided Content-Adaptive Transform for Image Compression

Shi,

Ye,

Wang

et al. 2024

Sensors

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The surge in image data has significantly increased the pressure on storage and transmission, posing new challenges for image compression technology. The structural texture of an image implies its statistical characteristics, which is effective for image encoding and decoding. Consequently, content-adaptive compression methods based on learning can better capture the content attributes of images, thereby enhancing encoding performance. However, learned image compression methods do not comprehensively account for both the global and local correlations among the pixels within an image. Moreover, they are constrained by rate-distortion optimization, which prevents the attainment of a compact representation of image attributes. To address these issues, we propose a syntax-guided content-adaptive transform framework that efficiently captures image attributes and enhances encoding efficiency. Firstly, we propose a syntax-refined side information module that fully leverages syntax and side information to guide the adaptive transformation of image attributes. Moreover, to more thoroughly exploit the global and local correlations in image space, we designed global–local modules, local–global modules, and upsampling/downsampling modules in codecs, further eliminating local and global redundancies. The experimental findings indicate that our proposed syntax-guided content-adaptive image compression model successfully adapts to the diverse complexities of different images, which enhances the efficiency of image compression. Concurrently, the method proposed has demonstrated outstanding performance across three benchmark datasets.

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Syntax-Guided Content-Adaptive Transform for Image Compression

Shi,

Ye,

Wang

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

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High performance holographic video compression using spatio-temporal phase unwrapping

Cited by 1 publication

References 51 publications

Syntax-Guided Content-Adaptive Transform for Image Compression

Syntax-Guided Content-Adaptive Transform for Image Compression

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