Redirigiendo al acceso original de articulo en 21 segundos...
Inicio  /  Information  /  Vol: 15 Par: 2 (2024)  /  Artículo
ARTÍCULO
TITULO

Compressive Sensing in Image/Video Compression: Sampling, Coding, Reconstruction, and Codec Optimization

Jinjia Zhou and Jian Yang    

Resumen

Compressive Sensing (CS) has emerged as a transformative technique in image compression, offering innovative solutions to challenges in efficient signal representation and acquisition. This paper provides a comprehensive exploration of the key components within the domain of CS applied to image and video compression. We delve into the fundamental principles of CS, highlighting its ability to efficiently capture and represent sparse signals. The sampling strategies employed in image compression applications are examined, emphasizing the role of CS in optimizing the acquisition of visual data. The measurement coding techniques leveraging the sparsity of signals are discussed, showcasing their impact on reducing data redundancy and storage requirements. Reconstruction algorithms play a pivotal role in CS, and this article reviews state-of-the-art methods, ensuring a high-fidelity reconstruction of visual information. Additionally, we explore the intricate optimization between the CS encoder and decoder, shedding light on advancements that enhance the efficiency and performance of compression techniques in different scenarios. Through a comprehensive analysis of these components, this review aims to provide a holistic understanding of the applications, challenges, and potential optimizations in employing CS for image and video compression tasks.

 Artículos similares

       
 
Nuha A. S. Alwan and Zahir M. Hussain    
This work combines compressive sensing and short word-length techniques to achieve localization and target tracking in wireless sensor networks with energy-efficient communication between the network anchors and the fusion center. Gradient descent locali... ver más
Revista: Information

 
Ziran Wei, Jianlin Zhang, Zhiyong Xu and Yong Liu    
According to the theory of compressive sensing, a single-pixel imaging system was built in our laboratory, and imaging scenes are successfully reconstructed by single-pixel imaging, but the quality of reconstructed images in traditional methods cannot me... ver más
Revista: Applied Sciences

 
Dongyue Yang, Chen Chang, Guohua Wu, Bin Luo and Longfei Yin    
Ghost imaging reconstructs the image based on the second-order correlation of the repeatedly measured light fields. When the observed object is moving, the consecutive sampling procedure leads to a motion blur in the reconstructed images. To overcome thi... ver más
Revista: Applied Sciences

 
Juan Paúl Inga Ortega,Anthony Yanza Verdugo,Christian Pucha Cabrera    
Este trabajo propone la aplicación de un estimador de canal basado en sensado compresivo (CS, del inglés Compressive Sensing) sobre un sistema que usa multiplexación por división de frecuencias ortogonales (OFDM, del inglés Orthogonal Frequency Division ... ver más

 
Bin Wang, Li Wang, Hao Yu and Fengming Xin    
The compressed sensing theory has been widely used in solving undetermined equations in various fields and has made remarkable achievements. The regularized smooth L0 (ReSL0) reconstruction algorithm adds an error regularization term to the smooth L0(SL0... ver más
Revista: Algorithms