Modelling of Depth Prediction Algorithm for Intra Prediction Complexity Reduction

Abstract

Video compression gained its relevance with the boon of the internet, mobile phones, variable resolution acquisition device etc. The redundant information is explored in initial stages of compression that’s is prediction. Inter prediction that is prediction within the frame generates high computational complexity when working with traditional signal processing procedures. The paper proposes the design of a deep convolutional neural network model to perform inter prediction by crossing out the flaws in the traditional method. It briefs the modeling of network, mathematics behind each stage and evaluation of the proposed model with sample dataset. The video frame’s coding tree unit (CTU) of 64x64 is the input, the model converts and store it as a 16-element vector with the goodness of CNN network. It gives an overview of deep depth decision algorithm. The evaluation process shows that the model performs better for compression with less computational complexity.

Extending Finch-Skea isotropic model to anisotropic domain in modified f(, T ) gravity

“…The Tolman-Kuchowicz interior possessing anisotropic fluid distribution has also been successfully developed by using the above linear model [42]. Some other actively discussed works are [43][44][45][46][47]. We have utilized the same model to formulate several acceptable compact interiors through the complexity-free constraint and a liner equation of state [48,49].…”

Anisotropic extensions of isotropic Finch–Skea metric in the charged modified gravity

“…where 1  represents an integration constant taken to be zero to avoid singularity at the center. Some different but interesting studies are [79][80][81][82][83]. The extended form of the radial component can be written as…”