61 research outputs found
Learned Image Compression with Generalized Octave Convolution and Cross-Resolution Parameter Estimation
The application of the context-adaptive entropy model significantly improves
the rate-distortion (R-D) performance, in which hyperpriors and autoregressive
models are jointly utilized to effectively capture the spatial redundancy of
the latent representations. However, the latent representations still contain
some spatial correlations. In addition, these methods based on the
context-adaptive entropy model cannot be accelerated in the decoding process by
parallel computing devices, e.g. FPGA or GPU. To alleviate these limitations,
we propose a learned multi-resolution image compression framework, which
exploits the recently developed octave convolutions to factorize the latent
representations into the high-resolution (HR) and low-resolution (LR) parts,
similar to wavelet transform, which further improves the R-D performance. To
speed up the decoding, our scheme does not use context-adaptive entropy model.
Instead, we exploit an additional hyper layer including hyper encoder and hyper
decoder to further remove the spatial redundancy of the latent representation.
Moreover, the cross-resolution parameter estimation (CRPE) is introduced into
the proposed framework to enhance the flow of information and further improve
the rate-distortion performance. An additional information-fidelity loss is
proposed to the total loss function to adjust the contribution of the LR part
to the final bit stream. Experimental results show that our method separately
reduces the decoding time by approximately 73.35 % and 93.44 % compared with
that of state-of-the-art learned image compression methods, and the R-D
performance is still better than H.266/VVC(4:2:0) and some learning-based
methods on both PSNR and MS-SSIM metrics across a wide bit rates.Comment: Accepted by signal processin
Fast and High-Performance Learned Image Compression With Improved Checkerboard Context Model, Deformable Residual Module, and Knowledge Distillation
Deep learning-based image compression has made great progresses recently.
However, many leading schemes use serial context-adaptive entropy model to
improve the rate-distortion (R-D) performance, which is very slow. In addition,
the complexities of the encoding and decoding networks are quite high and not
suitable for many practical applications. In this paper, we introduce four
techniques to balance the trade-off between the complexity and performance. We
are the first to introduce deformable convolutional module in compression
framework, which can remove more redundancies in the input image, thereby
enhancing compression performance. Second, we design a checkerboard context
model with two separate distribution parameter estimation networks and
different probability models, which enables parallel decoding without
sacrificing the performance compared to the sequential context-adaptive model.
Third, we develop an improved three-step knowledge distillation and training
scheme to achieve different trade-offs between the complexity and the
performance of the decoder network, which transfers both the final and
intermediate results of the teacher network to the student network to help its
training. Fourth, we introduce regularization to make the numerical
values of the latent representation more sparse. Then we only encode non-zero
channels in the encoding and decoding process, which can greatly reduce the
encoding and decoding time. Experiments show that compared to the
state-of-the-art learned image coding scheme, our method can be about 20 times
faster in encoding and 70-90 times faster in decoding, and our R-D performance
is also higher. Our method outperforms the traditional approach in
H.266/VVC-intra (4:4:4) and some leading learned schemes in terms of PSNR and
MS-SSIM metrics when testing on Kodak and Tecnick-40 datasets.Comment: Submitted to Trans. Journa
Improved Hybrid Layered Image Compression using Deep Learning and Traditional Codecs
Recently deep learning-based methods have been applied in image compression
and achieved many promising results. In this paper, we propose an improved
hybrid layered image compression framework by combining deep learning and the
traditional image codecs. At the encoder, we first use a convolutional neural
network (CNN) to obtain a compact representation of the input image, which is
losslessly encoded by the FLIF codec as the base layer of the bit stream. A
coarse reconstruction of the input is obtained by another CNN from the
reconstructed compact representation. The residual between the input and the
coarse reconstruction is then obtained and encoded by the H.265/HEVC-based BPG
codec as the enhancement layer of the bit stream. Experimental results using
the Kodak and Tecnick datasets show that the proposed scheme outperforms the
state-of-the-art deep learning-based layered coding scheme and traditional
codecs including BPG in both PSNR and MS-SSIM metrics across a wide range of
bit rates, when the images are coded in the RGB444 domain.Comment: Submitted to Signal Processing: Image Communicatio
Hollow mesoporous silica nanoparticles for intracellular delivery of fluorescent dye
In this study, hollow mesoporous silica nanoparticles (HMSNs) were synthesized using the sol-gel/emulsion approach and its potential application in drug delivery was assessed. The HMSNs were characterized, by transmission electron microscopy (TEM), Scanning Electron Microscopy (SEM), nitrogen adsorption/desorption and Brunauer-Emmett-Teller (BET), to have a mesoporous layer on its surface, with an average pore diameter of about 2 nm and a surface area of 880 m2/g. Fluorescein isothiocyanate (FITC) loaded into these HMSNs was used as a model platform to assess its efficacy as a drug delivery tool. Its release kinetic study revealed a sequential release of FITC from the HMSNs for over a period of one week when soaked in inorganic solution, while a burst release kinetic of the dye was observed just within a few hours of soaking in organic solution. These FITC-loaded HMSNs was also found capable to be internalized by live human cervical cancer cells (HeLa), wherein it was quickly released into the cytoplasm within a short period of time after intracellular uptake. We envision that these HMSNs, with large pores and high efficacy to adsorb chemicals such as the fluorescent dye FITC, could serve as a delivery vehicle for controlled release of chemicals administered into live cells, opening potential to a diverse range of applications including drug storage and release as well as metabolic manipulation of cells
Insight-HXMT observations of Swift J0243.6+6124 during its 2017-2018 outburst
The recently discovered neutron star transient Swift J0243.6+6124 has been
monitored by {\it the Hard X-ray Modulation Telescope} ({\it Insight-\rm HXMT).
Based on the obtained data, we investigate the broadband spectrum of the source
throughout the outburst. We estimate the broadband flux of the source and
search for possible cyclotron line in the broadband spectrum. No evidence of
line-like features is, however, found up to . In the absence of
any cyclotron line in its energy spectrum, we estimate the magnetic field of
the source based on the observed spin evolution of the neutron star by applying
two accretion torque models. In both cases, we get consistent results with
, and peak luminosity of which makes the source the first Galactic ultraluminous
X-ray source hosting a neutron star.Comment: publishe
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