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

Multiple Description Video Coding Using Joint Frame Duplication/Interpolation

Multiple description coding (MDC) is a promising alternative to combatting information loss without any retransmission. In this paper, an effective MD video codec is designed based on temporal pre- and post-processing of video sequences without modifying the actual coding process itself, which makes it compatible with the current standard source or channel codec. For ease of post-processing, motion-compensated interpolation (MCI) based on piecewise uniform motion assumption is adopted to estimate the lost frame in side decoding. Accordingly, to match the post-processing, in the pre-processing joint frame duplication/interpolation is first applied to the original video data before performing odd/even frame splitting, which attempts to make the motion variety in the generated descriptions piecewise uniformly thus achieving better side reconstructed quality based on MCI. The experimental results exhibit better performance of the proposed scheme than some other tested schemes, in both the on-off channel environment and packet loss network

A Novel Apoptosis Correlated Molecule: Expression and Characterization of Protein Latcripin-1 from Lentinula edodes C91–3

An apoptosis correlated molecule—protein Latcripin-1 of Lentinula edodes C91–3—was expressed and characterized in Pichia pastoris GS115. The total RNA was obtained from Lentinula edodes C91–3. According to the transcriptome, the full-length gene of Latcripin-1 was isolated with 3′-Full Rapid Amplification of cDNA Ends (RACE) and 5′-Full RACE methods. The full-length gene was inserted into the secretory expression vector pPIC9K. The protein Latcripin-1 was expressed in Pichia pastoris GS115 and analyzed by Sodium Dodecylsulfonate Polyacrylate Gel Electrophoresis (SDS-PAGE) and Western blot. The Western blot showed that the protein was expressed successfully. The biological function of protein Latcripin-1 on A549 cells was studied with flow cytometry and the 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyl-tetrazolium Bromide (MTT) method. The toxic effect of protein Latcripin-1 was detected with the MTT method by co-culturing the characterized protein with chick embryo fibroblasts. The MTT assay results showed that there was a great difference between protein Latcripin-1 groups and the control group (p < 0.05). There was no toxic effect of the characterized protein on chick embryo fibroblasts. The flow cytometry showed that there was a significant difference between the protein groups of interest and the control group according to apoptosis function (p < 0.05). At the same time, cell ultrastructure observed by transmission electron microscopy supported the results of flow cytometry. The work demonstrates that protein Latcripin-1 can induce apoptosis of human lung cancer cells A549 and brings new insights into and advantages to finding anti-tumor proteins