Video compression is indispensable to web streaming and memory storage.Most video\rcompression technology has difficulty to achieve high quality video at lower bit\rrates.Apparently,limited transmission bandwidth and network resources often degrade\rvideo signals.Thus the goal of my research was to enhance video degrade video\rsignals.Thus the goal of my research was to enhance video compression performance and\rto improve visual quality.It is hypothesized that the reduction in neighboring pixels\rcoding,and humans perceptual mechanisms(psychovisual)redundancy could produce a\rlow-complexity geometry streams for animated visual objects.A set of algorithms is\rdeveloped to parse bidirectional interpolation pixels into their characteristic cells,which\rvary in spectral energy and wavelength.The bits contained in these cells are vectorized and\rtransformed recursively to identify lower correlations among vector arrarys for blocks\rfiltering.DCT function calculates energy ratios between high spatial frequency and low\rspatial frequency,to devote most of the highest spatial frequency bits with the calculated\renergy ratios.A variable quantization method is used to measure the sensitivity of colors\rand its intensity ratios to restore any missing high spatial frequency pixels.presnted in\rmathematical intrinsic.This approach leads to the ability to compress video data that\rnormally require a large amount of memory to store and high bandwidth to\rtransmit,Results form the enhanced video compression experiment have attained\r0.1bpp(256kbps,25fps)without noticeable effects comparable to the video compression\rtechnique that achieved 0.5bpp(1.5Mbps,25fps)in use today.
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