Graph-based transforms based on prediction inaccuracy modeling for pathology image coding

Digital pathology images are multi giga-pixel color images that usually require large amounts of bandwidth to be transmitted and stored. Lossy compression using intra - prediction offers an attractive solution to reduce the storage and transmission requirements of these images. In this paper, we evaluate the performance of the Graph - based Transform (GBT) within the context of block - based predictive transform coding. To this end, we introduce a novel framework that eliminates the need to signal graph information to the decoder to recover the coefficients. This is accomplished by computing the GBT using predicted residual blocks, which are predicted by a modeling approach that employs only the reference samples and information about the prediction mode. Evaluation results on several pathology images, in terms of the energy preserved and MSE when a small percentage of the largest coefficients are used for reconstruction, show that the GBT can outperform the DST and DCT

Graph-based transform with weighted self-loops for predictive transform coding based on template matching

This paper introduces the GBT-L, a novel class of Graph-based Transform within the con- text of block-based predictive transform coding. The GBT-L is constructed using a 2D graph with unit edge weights and weighted self-loops in every vertex. The weighted self- loops are selected based on the residual values to be transformed. To avoid signalling any additional information required to compute the inverse GBT-L, we also introduce a coding framework that uses a template-based strategy to predict residual blocks in the pixel and residual domains. Evaluation results on several video frames and medical images, in terms of the percentage of preserved energy and mean square error, show that the GBT-L can outperform the DST, DCT and the Graph-based Separable Transfor

Graph-based transforms based on graph neural networks for predictive transform coding

This paper introduces the GBT-NN, a novel class of Graph-based Transform within the context of block-based predictive transform coding using intra-prediction. The GBT-NN is constructed by learning a mapping function to map a graph Laplacian representing the covariance matrix of the current block. Our objective of learning such a mapping function is to design a GBT that performs as well as the KLT without requiring to explicitly compute the covariance matrix for each residual block to be transformed. To avoid signalling any additional information required to compute the inverse GBT-NN, we also introduce a coding framework that uses a template-based prediction to predict residuals at the decoder. Evaluation results on several video frames and medical images, in terms of the percentage of preserved energy and mean square error, show that the GBT-NN can outperform the DST and DCT

Graph-based transform based on 3D convolutional neural network for intra-prediction of imaging data

This paper presents a novel class of Graph-based Transform based on 3D convolutional neural networks (GBT-CNN) within the context of block-based predictive transform coding of imaging data. The proposed GBT-CNN uses a 3D convolutional neural network (3D- CNN) to predict the graph information needed to compute the transform and its inverse, thus reducing the signalling cost to reconstruct the data after transformation. The GBT- CNN outperforms the DCT and DCT/DST, which are commonly employed in current video codecs, in terms of the percentage of energy preserved by a subset of transform coefficients, the mean squared error of the reconstructed data, and the transform coding gain according to evaluations on several video frames and medical images

Graph-based transform based on neural networks for intra-prediction of imaging data

This paper introduces a novel class of Graph-based Trans-form based on neural networks (GBT-NN) within the con-text of block-based predictive transform coding of imaging data. To reduce the signalling overhead required to recon-struct the data after transformation, the proposed GBT-NN predicts the graph information needed to compute the inverse transform via a neural network. Evaluation results on several video frames and medical images, in terms of the percentage of energy preserved by a sub-set of transform coefficients and the mean squared error of the reconstructed data, show that the GBT-NN can outperform the DCT and DST, which are widely used in modern video codecs

Room temperature synthesis of PdxNi100-x nanoalloy: superior catalyst for electro-oxidation of methanol and ethanol

Bimetallic Pd-Ni alloy nanoparticles with tunable dimensions, unique composition and excellent electrocatalytic activity towards methanol and ethanol oxidation reaction (MOR and EOR) in alkali, were successfully synthesized by co-reduction of metal precursors in strong alkali medium at room temperature. X-ray diffraction profiles typically signify alloy structure of the particles. Microscopy, diffraction and spectroscopy studies further conform the successful formation of Pd-Ni nanoalloy of determined diameter and morphology. The compositions of this alloy nanoparticles can be easily tuned by typically varying the Pd2+/Ni2+ molar ratio. The mol% of Ni present in the Pd-Ni bimetallic nanoalloy portrays a key role on the catalytic activity for MOR and EOR in alkali. Pd70Ni30/C catalyst exhibits the optimum synergic catalytic activity with improved oxidation of carbonaceous intermediates. Chronoamperometric study satisfactorily proves that Pd70Ni30/C is quite stable at ambient temperature and can be used as anode for MOR and EOR. GRAPHICS]

Room temperature synthesis of Pd-Cu nanoalloy catalyst with enhanced electrocatalytic activity for the methanol oxidation reaction

Bimetallic Pd-Cu nanoparticles with controllable size, composition and superior electrocatalytic activity towards methanol oxidation reaction (MOR) in alkaline media were synthesized by a facile room temperature soft chemical method. Pd-Cu nanoalloy catalysts (17-25 nm) were prepared by templating Pd2+ and Cu2+ with ethylenediaminetetraacetic acid (EDTA) followed by controlled chemical reduction of metal-EDTA complex with hydrazine in highly basic medium at room temperature without any protection with an inert gas atmosphere. X-ray diffraction pattern indicates that the particles have an alloy structure. FESEM, EDX, HRTEM and SAED results further support the formation of Pd-Cu nanoalloy. Compliance of the lattice spacing corresponding to (1 1 1) and (2 0 0) planes obtained from HRTEM and SAED results with XRD data confirms the formation of Pd-Cu nanoalloy at room temperature. By varying the Pd2+/Cu2+ molar ratio the compositions of these alloy NPs can be easily adjusted. The amount of Cu present in the Pd-Cu nanoalloy plays a key role on the catalytic activity for MOR in alkaline media. Upon increase in concentration of Cu in the Pd-Cu nanoalloy, the catalytic activity towards MOR increases, reaches a maximum in Pd-Cu(3:1) nanoalloy and then decreases. The maximum mass activity of 659.4mA mg(-1) of Pd obtained in the present work for the synthesized Pd-Cu(3 :1) nanoalloy is much better than literature reported value. Multi-scan cyclic voltammetry and chronoamperometric studies confirm the electrocatalytic activity and stability of the synthesized electrode material. The present catalyst with low noble metal content promotes its practical use as anodes under alkaline conditions in direct methanol fuel cells (DMFC). (C) 2014 Elsevier B.V. All rights reserved

Improved catalysis of room temperature synthesized Pd-Cu alloy nanoparticles for anodic oxidation of ethanol in alkaline media

In search for cost-effective anode material of direct ethanol fuel cell, nanoparticles of Pd, Cu and PdxCu1-x alloy have been synthesized in absence of any capping agent by one pot reduction and co-reduction of respective metal precursors at room temperature. The spectroscopic and microscopic studies reveal that the particles are loosely agglomerated interconnected spherical shaped nanoalloy with a radius in the range of 17-25 nm. Electrochemical studies of graphite supported synthesized nanoparticles reveal that Pd0.90Cu0.10/C is the best and exhibits synergistic catalytic activity. This electrode shows the highest exchange current density for ethanol oxidation reaction and higher catalytic activity for oxidizing acetaldehyde. A reaction mechanism is also proposed in the study. (C) 2014 Elsevier Ltd. All rights reserved