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A novel two-layer motion estimation which searches motion vectors on two layers with partial distortion measures in order to reduce the overwhelming computational complexity of motion estimation (ME) in video coding is proposed. A layer is an image which is derived from the reference frame such that the summation of a block of pixels in the reference frame determines the point of a layer. It has been noted on different video sequences that many motion vectors on the layers are the same as those searched on the reference frame. Experimental results on a wide variety of video sequences show that the proposed algorithm achieves both fast speed and good motion prediction quality when compared with the state-of-the-art fast block matching algorithms.Introduction: Most of the fast search motion estimation (ME) algorithms endeavour to reduce the computational cost of ME greatly by checking only a few search points inside the search area by using the full distortion measure. Some of these algorithms [1-4] have reduced as many search points as possible to reduce the computational cost of ME. It is very difficult to reduce the number of search points further without degrading motion prediction quality. The computational cost of ME can be cut down not only by reducing the number of search points, but also diminishing the number of computations in the block distortion measure. This implies a partial distortion measure instead of the full distortion measure used at each search point. In this Letter, a two-layer ME (TME) is proposed. This algorithm first constructs the layers from the reference frame so as to facilitate the calculation of partial distortion measures on the layers with a lesser number of computations. Later, it performs ME by computing the partial distortion measures on the layer.

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In the realm of the emergence and spread of infectious diseases with pandemic potential throughout the history, plenty of pandemics (and epidemics), from the plague to AIDS (1981) and SARS (in 2003) to the bunch of COVID variants, have tormented mankind. Though plenty of technological innovations are overwhelmingly progressing to curb them—a significant number of such pandemics astounded the world, impacting billions of lives and posing uncovered challenges to healthcare organizations and clinical pathologists globally. In view of addressing these limitations, a critically exhaustive review is performed to signify the prospective role of technological advancements and highlight the implicit problems associated with rendering best quality lifesaving treatments to the patient community. The proposed review work is conducted in two parts. Part 1 is essentially focused upon discussion of advanced technologies akin to artificial intelligence, Big Data, block chain technology, open-source technology, cloud computing, etc. Research works governing applicability of these technologies in solving many uncovered healthcare issues prominently faced by doctors and surgeons in the fields of cardiology, medicine, neurology, orthopaedics, paediatrics, gynaecology, psychiatry, plastic surgery, etc., as well as their role in curtailing the spread of numerous infectious, pathological, neurotic maladies is thrown light off. Boundary conditions and implicitly associated challenges substantiated by remedies coupled with future directions are presented at the end.