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Abstract-A video display device having a lower number of bits per pixel than that required by the video to be displayed quantizes the video prior to its display. Halftoning can perform this quantization while attempting to reduce the visibility of certain quantization artifacts. Quantization artifacts are, nevertheless, not eliminated. A temporal artifact known as dirtywindow-effect can be commonly observed in medium framerate binary video halftones. In this paper, we propose video halftone enhancement algorithms to reduce dirty-window-effect. We assess the performance of the proposed algorithms by presenting objective measures for dirty-window-effect in the original and the improved halftone videos. The expected contributions of this paper include three medium frame-rate binary video halftone enhancement algorithms that (1) reduce dirty-windoweffect under a spatial quality constraint, (2) reduce dirty-windoweffect under a spatial quality constraint with reduced complexity, and (3) reduce dirty-window-effect under spatial and temporal quality constraints.Index Terms-video halftoning, temporal artifacts, dirtywindow-effect.

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Kidney is an important organ in human body as it maintains the nutrients and fluid balance in our body. It is extremely beneficial if its dysfunctionality is diagnosed at an early stage. Iridology provides a pathway to examine the kidney disease through iris images. Therefore, in this work we proposed the Iris-based Kidney Disease Identification System (IKDIS). The IKDIS would aid in identifying abnormalities through iris images an input which would be followed by application of deep neural network model for assessment. This type of diagnostic system without involving any instruments for assessment of human body organs is much popular these days. The data of 49 patients gives promising results of IKDIS, achieving overall accuracy of 86.9% during the experiment.