Mostrar el registro sencillo del ítem

dc.contributor.authorPan, Shaoming
dc.contributor.authorGu, XiaoLin
dc.contributor.authorChong, Yanwen
dc.contributor.authorGuo, Yuanyuan
dc.date2022-09
dc.date.accessioned2022-10-24T11:13:28Z
dc.date.available2022-10-24T11:13:28Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/13708
dc.description.abstractIn content-based image compression, the importance map guides the bit allocation based on its ability to represent the importance of image contents. In this paper, we improve the representational power of importance map using Squeeze-and-Excitation (SE) block, and propose multi-depth structure to reconstruct non-important channel information at low bit rates. Furthermore, Dynamic Receptive Field convolution (DRFc) is introduced to improve the ability of normal convolution to extract edge information, so as to increase the weight of edge content in the importance map and improve the reconstruction quality of edge regions. Results indicate that our proposed method can extract an importance map with clear edges and fewer artifacts so as to provide obvious advantages for bit rate allocation in content-based image compression. Compared with typical compression methods, our proposed method can greatly improve the performance of Peak Signal-to-Noise Ratio (PSNR), structural similarity (SSIM) and spectral angle (SAM) on three public datasets, and can produce a much better visual result with sharp edges and fewer artifacts. As a result, our proposed method reduces the SAM by 42.8% compared to the recently SOTA method to achieve the same low bpp (0.25) on the KAIST dataset.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)es_ES
dc.relation.urihttps://www.ijimai.org/journal/bibcite/reference/3153es_ES
dc.rightsopenAccesses_ES
dc.subjectcompressiones_ES
dc.subjectdynamic strategyes_ES
dc.subjectmapses_ES
dc.subjecthyperspectral imagees_ES
dc.subjectmulti-Depthes_ES
dc.subjectIJIMAIes_ES
dc.titleContent-Based Hyperspectral Image Compression Using a Multi-Depth Weighted Map With Dynamic Receptive Field Convolutiones_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttps://doi.org/10.9781/ijimai.2022.08.004


Ficheros en el ítem

Thumbnail

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem