English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Inter-Prediction is used effectively in multiple standards, including H.264and HEVC (also known as H.265). It leverages correlation between blocks ofconsecutive video frames in order to perform motion compensation and thuspredict block pixel values and reduce transmission bandwidth. In order toreduce the magnitude of the transmitted Motion Vector (MV) and thus reducebandwidth, the encoder utilizes Predicted Motion Vector (PMV), which is derivedby taking the median vector of the corresponding MVs of the neighboring blocks.In this research, we propose innovative methods, based on neural networksprediction, for improving the accuracy of the calculated PMV. We begin byshowing a straightforward approach of calculating the best matching PMV andsignaling its neighbor block index value to the decoder while reducing thenumber of bits required to represent the result without adding any computationcomplexity. Then we use a classification Fully Connected Neural Networks (FCNN)to estimate from neighbors the PMV without requiring signaling and show theadvantage of the approach when employed for high motion movies. We demonstratethe advantages using fast forward movies. However, the same improvements applyto camera streams of autonomous vehicles, drone cameras, Pan-Tilt-Zoom (PTZ)cameras, and similar applications whereas the MVs magnitudes are expected to belarge. We also introduce a regression FCNN to predict the PMV. We calculateHuffman coded streams and demonstrate an order of ~34% reduction in number ofbits required to transmit the best matching calculated PMV without reducing thequality, for fast forward movies with high motion.

Improvements of Motion Estimation and Coding using Neural Networks