Abstract
In a context marked by the proliferation of smartphones and multimedia applications, the processing and transmission of images have become a real problem. Image compression is the first approach to address this problem, it nevertheless suffers from its inability to adapt to the dynamics of limited environments, consisting mainly of mobile equipment and wireless networks. In this work, we propose a stochastic model to gradually estimate an image upon information on its pixels that are transmitted progressively. We consider this transmission as a dynamical process, where the sender pushes the data in decreasing significance order. In order to adapt to network conditions and performances, instead of truncating the pixels, we suggest a new method called Fast Reconstruction Method by Kalman Filtering (FRM-KF) consisting of recursive inference of the not yet received layers belonging to a sequence of bitplanes. After empirical analysis, we estimate parameters of our model which is a linear discrete Kalman Filter. We assume the initial law of information to be the uniform distribution on the set [0, 255] corresponding to the range of gray levels. The performances of FRM-KF method have been evaluated in terms of the ratios in the quality of data image/size sent and in the quality of image/time required for treatment. A high quality was reached faster with relatively small data (less than 10% of image data is needed to obtain up to the sixth-quality image). The time for treatment also decreases faster with number of received layers. However, we found that the time of image treatment might be large starting from a image resolution of 1024 * 1024. Hence, we recommend FRM-KF method for resolutions less or equal to 512 * 512. A statistical comparative analysis reveals that FRM-KF is competitive and suitable to be implemented on limited resource environments.
Highlights
Transmission of digital images has been widely studied, since the early years of the Internet [1]
In order to adapt to network conditions and performances, instead of truncating the pixels, we suggest a new method called Fast Reconstruction Method by Kalman Filtering (FRM-Kalman filtering (KF)) consisting of recursive inference of the not yet received layers belonging to a sequence of bitplanes
The performances of FRM-KF method have been evaluated in terms of the ratios in the quality of data image/size sent and in the quality of image/time required for treatment
Summary
Transmission of digital images has been widely studied, since the early years of the Internet [1]. The primary objective of PIT is to transmit a significant and interpretable core of the image and subsequently transmit complements layers in order to gradually improve the quality. This method requires a preparation of the image to be transmitted. We are interested in the progressive transmission and refinement of still images, as a process that adapts to low quality network service. The display of an image is considered as a progressive process in order to adapt to network conditions. The sender selects the image data, layer by layer, from most significant to the least one, depending on the quality of the desired image at the receiver.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call