A Quantitative Approach To The Temporal Dependency in Video Coding
Jingning Han, Paul Wilkins, Yaowu Xu, James Bankoski

TL;DR
This paper introduces a quantitative method to better estimate temporal dependency in video coding, especially at low to medium bit-rates, leading to improved compression performance.
Contribution
It proposes a new approach that accurately measures temporal dependency by evaluating rate and distortion using original and reconstructed reference blocks.
Findings
More accurate dependency estimation across various bit-rates
Significant coding performance improvements demonstrated
Effective in low to medium bit-rate scenarios
Abstract
Motion compensated prediction is central to the efficiency of video compression. Its predictive coding scheme propagates the quantization distortion through the prediction chain and creates a temporal dependency. Prior research typically models the distortion propagation based on the similarity between original pixels under the assumption of high resolution quantization. Its efficacy in the low to medium bit-rate range, where the quantization step size is largely comparable to the magnitude of the residual signals, is questionable. This work proposes a quantitative approach to estimating the temporal dependency. It evaluates the rate and distortion for each coding block using the original and the reconstructed motion compensation reference blocks, respectively. Their difference effectively measures how the quantization error in the reference block impacts the coding efficiency of the…
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Taxonomy
TopicsVideo Coding and Compression Technologies · Advanced Data Compression Techniques · Advanced Vision and Imaging
