Graded quantization for multiple description coding of compressive measurements

Compressed sensing (CS) is an emerging paradigm for acquisition of compressed representations of a sparse signal. Its low complexity is appealing for resource-constrained scenarios like sensor networks. However, such scenarios are often coupled with unreliable communication channels and providing robust transmission of the acquired data to a receiver is an issue. Multiple description coding (MDC) effectively combats channel losses for systems without feedback, thus raising the interest in developing MDC methods explicitly designed for the CS framework, and exploiting its properties. We propose a method called Graded Quantization (CS-GQ) that leverages the democratic property of compressive measurements to effectively implement MDC, and we provide methods to optimize its performance. A novel decoding algorithm based on the alternating directions method of multipliers is derived to reconstruct signals from a limited number of received descriptions. Simulations are performed to assess the performance of CS-GQ against other methods in presence of packet losses. The proposed method is successful at providing robust coding of CS measurements and outperforms other schemes for the considered test metrics

Deep Multiple Description Coding by Learning Scalar Quantization

In this paper, we propose a deep multiple description coding framework, whose quantizers are adaptively learned via the minimization of multiple description compressive loss. Firstly, our framework is built upon auto-encoder networks, which have multiple description multi-scale dilated encoder network and multiple description decoder networks. Secondly, two entropy estimation networks are learned to estimate the informative amounts of the quantized tensors, which can further supervise the learning of multiple description encoder network to represent the input image delicately. Thirdly, a pair of scalar quantizers accompanied by two importance-indicator maps is automatically learned in an end-to-end self-supervised way. Finally, multiple description structural dissimilarity distance loss is imposed on multiple description decoded images in pixel domain for diversified multiple description generations rather than on feature tensors in feature domain, in addition to multiple description reconstruction loss. Through testing on two commonly used datasets, it is verified that our method is beyond several state-of-the-art multiple description coding approaches in terms of coding efficiency.Comment: 8 pages, 4 figures. (DCC 2019: Data Compression Conference). Testing datasets for "Deep Optimized Multiple Description Image Coding via Scalar Quantization Learning" can be found in the website of https://github.com/mdcnn/Deep-Multiple-Description-Codin

Robust video coder solution for wireless streaming: applications in Gaussian channels

With the technological progress in wireless communications seen in the past decade, the miniaturization of personal computers was imminent. Due to the limited availability of resources in these small devices, it has been preferable to stream the media over widely deployed networks like the Internet. However, the conventional protocols used in physical and data-link layers are not adequate for reliable video streaming over noisy wireless channels. There are several popular and well-studied mechanisms for addressing this problem, one of them being Multiple-Description-Coding. However, proposed solutions are too specialized, focusing the coding of either motion or spatial information; thus failing to address the whole problem, that is - the robust video coding. In this thesis a novel MDC video coder is presented, which was developed during an internship at the I3S laboratory - France. The full coding scheme is capable of robust transmission of Motion-Vectors and wavelet-subband information over noisy wireless channels. The former is accomplished by using a MAP-based MD-decoding algorithm available in literature, while the robust transmission of wavelet-subbands is achieved using a state-of-the-art registry-based JPEG-2000 MDC. In order to e ciently balance MV information between multiple descriptions, a novel R/D-optimizing MD bitallocation scheme is presented. As it is also important to e ciently distribute bits between subband and motion information, a global subband/motion-vector bit-allocation technique found in literature was adopted and improved. Indeed, this thesis would not be complete without the presentation of produced streams as well as of a set of backing scienti c results

Confocal microscopic image sequence compression using vector quantization and 3D pyramids

The 3D pyramid compressor project at the University of Glasgow has developed a compressor for images obtained from CLSM device. The proposed method using a combination of image pyramid coder and vector quantization techniques has good performance at compressing confocal volume image data. An experiment was conducted on several kinds of CLSM data using the presented compressor compared to other well-known volume data compressors, such as MPEG-1. The results showed that the 3D pyramid compressor gave higher subjective and objective image quality of reconstructed images at the same compression ratio and presented more acceptable results when applying image processing filters on reconstructed images