Compressed Sensing-Based Distributed Image Compression

Abstract

In this paper, a new distributed block-based image compression method based on the principles of compressed sensing (CS) is introduced. The coding and decoding processes are performed entirely in the CS measurement domain. Image blocks are classified into key and non-key blocks and encoded at different rates. The encoder makes use of a new adaptive block classification scheme that is based on the mean square error of the CS measurements between blocks. At the decoder, a simple, but effective, side information generation method is used for the decoding of the non-key blocks. Experimental results show that our coding scheme achieves better results than existing CS-based image coding methods.

A Hardware-Efficient Vector Quantizer Based on Self-Organizing Map for High-Speed Image Compression

“…In our design, if the size of pixel-block is defined as 8 × 8, the clock cycles to encode each pixel-block can be calculated as 39 according to Equation (6). Hence, the clock cycles to encode a frame of the grayscale 512 × 512-pixel image will be 159,744, and the corresponding time can be calculated as 2 ms with a maximum frequency of 79.8 MHz.…”

“…Generally, in high-speed vision systems, the challenges of insufficient bandwidth and storage are increasingly severe and gradually become the bottlenecks. In practice, image compression is widely considered as an effective approach to relieving the above-mentioned problems since it can reduce the data to a more manageable level before the image sequences are transmitted [6,7].…”

Data compression in ViSAR sensor networks using non-linear adaptive weighting

“…In addition, there are many other applications for interpolation algorithms, e.g., data hiding [14][15][16][17][18], interpolation-based image denoising and demosaicking [19][20][21], SDTV to HDTV conversion (SD2HD) [2] in video processing, color processing [22], information fusion [8,9], and shadow detection [23] which can be assisted by ALMMSE algorithm. As we mentioned, the main focus of this research is towards interpolation-based image/video compression [10,24]. For compression, we firstly down-sample video frames to reduce the information size at the sender side and then reconstruct them using an interpolator at the receiver side.…”

Image Compression Based on Compressive Sensing Using Wavelet Lifting Scheme

“…The following notation [8] as we have ݂ = ሼ݂ ଵ , … … … , ݂ ே ሽ be ܰ real-valued samples of a signal, which can be represented by the transform coefficients, ‫.ݔ‬ That is,…”

“…The concept of compressive sensing (CS) is to acquire significant information directly without first sampling the signal in the traditional sense. It has been shown that if the signal is "sparse" or compressible, then the acquired information is sufficient to reconstruct the original signal with a high probability [8] [10] [11]. Sparsity is defined with respect to an appropriate basis, such as DCT or WT for that signal.…”