REPro.JPEG: a new image compression approach based on reduction/expansion image and JPEG compression for dermatological medical images

Amri, Hédi; Khalfallah, Ali; Lapayre, Jean-Christophe; Bouhlel, Med Salim

doi:10.1080/13682199.2017.1289620

Cited by 6 publications

(1 citation statement)

References 29 publications

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“…Document 5 uses CNN to reduce the artifacts produced by the Joint Photographic Experts Group (JPEG) compression of holographic images, and solves the image quality degradation caused by the loss of some high-frequency features in the compression process. Literature 6 uses deep learning at the decoding end to reconstruct the sparse image obtained from the coding end into a light field image.…”

Section: Introductionmentioning

confidence: 99%

Design of mobile communication image transmission compression algorithm based on convolutional neural network

Zhu

2024

Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023)

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With the popularization of mobile communication applications, image transmission plays an increasingly important role in communication. However, due to the limitation of communication bandwidth and storage resources, efficient image compression has become an urgent need. The purpose of this study is to design an innovative mobile communication image transmission compression algorithm by introducing the deep learning technology of convolutional neural network (CNN). In this algorithm, through the feature learning of convolution level and the hierarchical structure of CNN, the abstract features in the image are gradually extracted. This feature extraction process can not only capture the texture and structure information of the image effectively, but also has the advantage of resisting the common information loss problem in traditional compression methods. In order to enhance the learning ability of the network, we introduce residual connection, which improves the network's long-range dependence and attention to important areas. Experimental results show that compared with traditional compression methods, the algorithm based on CNN can maintain higher image quality at the same compression rate. The algorithm is more universal and can meet the needs of mobile communication image transmission in different types and scenarios. In addition, the impact on transmission quality is relatively small, and it can maintain good visual effects even at high compression rate.

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Section: Introductionmentioning

confidence: 99%