Digital Signal, Image and Video Processing for Emerging Multimedia Technology
Kim, Byung-Gyu (editor)
This book presents collective works published in the recent Special Issue (SI) entitled " Digital Signal, Image and Video Processing for Emerging Multimedia Technology". These works address the emerging technology in signal processing and its new aspects, as well as the related applications. Recent developments in image/video-based deep learning technology have enabled new services in the field of multimedia and recognition technology. The applications vary and range from digital signal processing to image, video and multimedia signal processing, also including object classification, learning mechanism design and data security. Recent advances in numerical, theoretical and experimental methodologies are presented within the scope of the current book, along with the finding of new learning methods and new methodological developments and their limitations. This book brings together a collection of inter-/multidisciplinary works applied to many classification and data security applications in a coherent manner.
Keywordsclosed circuit television (CCTV); character order preserving; cloud system; privacy risk; security; video surveillance; 3D; depth map; inter-component prediction; MVD; reversible data hiding; texture; wavelet analysis; deep learning; super-resolution; deep neural architecture; pattern mining; multi-scale analysis; reversible data hiding (RDH); image processing; cloud computing; public key cryptography (PKC); classification; content–based image retrieval; genetic algorithms; image retrieval; image classification; Wiener-Granger causality; block-compressive sensing (BCS); saliency; error analysis; flexible partitioning; step-less adaptive sampling; non-linear filters; MCV and MLV filters; de-noising; noise removal; edge preserving; video coding; motion estimation; motion compensation; affine motion model; perspective motion model; VVC; quantization (signal); channel allocation; scalable video coding; convolution neural network; scene recognition; vector of locally aggregated descriptor; weakly supervised attention map; fire and smoke detection; spatial and temporal; wavelet transform; coefficient of variation; image steganalysis; WOW; UNIWARD; ternary classification; convolutional neural network (CNN); bayesian optimization; gaussian process; learning rate; acauisition function; machine learning; moving object; image stabilization; object detection; optical flow; surveillance; UAVs; multiview high efficiency video coding; ρ model; bit allocation; rate control; image similarity; frame complexity; image deblurring; generative adversarial network; Wasserstein distance; adversarial loss; perceptual loss; sentiment analysis; social media; lexicon; image fusion; multi-focus; trimaps; focus maps; VisDrone2019; aerial imagery; Faster R-CNN; SSD; RFCN; YOLOv3; RetinaNet; SNIPER; CenterNet; wrist-mounted DiverPAD; electrical insulator; capacitive touchscreen; marine leisure activities; convolutional neural networks; pattern recognition; low light; image restoration; denoise; noise reduction; deep leaning; multiple feature; dependency detection; surveillance system; n/a
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Publication date and placeBasel, Switzerland, 2021
History of engineering & technology