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

Data compression with application to geo-location

A common way to locate an emitter within a wireless sensor network requires the estimation of time-difference-of-arrival (TDOA) parameters using data collected by a set of spatially separated sensors. Compressing the data that is shared among the sensors can provide tremendous savings in terms of the energy and transmission latency. Traditional MSE and perceptual based data compression schemes fail to accurately capture the effects of compression on the TDOA estimation task; therefore, it is necessary to investigate compression algorithms suitable for TDOA parameter estimation. This thesis explores the effects of data compression on TDOA parameter estimation accuracy. The first part of this document investigates the decimation of band-limited communication signals which are oversampled to achieve high precision in the TDOA estimate. In the second part, we follow the work of [19-22] in implementing a Fisher Information-based subband encoding scheme, an approach that has been shown to provide better results than the traditional MSE-based approach. A pseudo-QMF filter bank [8] is implemented, which is computationally more efficient than wavelet packet filter banks, at the cost of relaxing perfect reconstruction conditions. Additionally, a suboptimal bit allocation algorithm is developed which further lessens the sensor resource requirements for compression

S-TREE: Self-Organizing Trees for Data Clustering and Online Vector Quantization

This paper introduces S-TREE (Self-Organizing Tree), a family of models that use unsupervised learning to construct hierarchical representations of data and online tree-structured vector quantizers. The S-TREE1 model, which features a new tree-building algorithm, can be implemented with various cost functions. An alternative implementation, S-TREE2, which uses a new double-path search procedure, is also developed. S-TREE2 implements an online procedure that approximates an optimal (unstructured) clustering solution while imposing a tree-structure constraint. The performance of the S-TREE algorithms is illustrated with data clustering and vector quantization examples, including a Gauss-Markov source benchmark and an image compression application. S-TREE performance on these tasks is compared with the standard tree-structured vector quantizer (TSVQ) and the generalized Lloyd algorithm (GLA). The image reconstruction quality with S-TREE2 approaches that of GLA while taking less than 10% of computer time. S-TREE1 and S-TREE2 also compare favorably with the standard TSVQ in both the time needed to create the codebook and the quality of image reconstruction.Office of Naval Research (N00014-95-10409, N00014-95-0G57