Video Encoding Motion Compensation Filtering
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Solution Overview
Problem
Video encoding efficiency is compromised when motion blur occurs, as sharper edges in reference frames result in wider spectra and less efficient encoding, leading to increased bitstream size and complexity.
Innovation Solution
A method is introduced where the encoder and decoder analyze motion compensated prediction samples to infer filtering parameters, such as anisotropy and filter direction, to apply appropriate filtering during the encoding and decoding processes, using a smoothing filter to reduce residuals and improve coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion compensated prediction is used with sharp reference frames, then prediction accuracy is improved, but encoding efficiency deteriorates due to wider spectrum residuals
Solution Approach 1:
The patent applies a blurring filter to the reference frame to modify its spectral characteristics. This parameter change in the reference frame's sharpness reduces the spectral width of residuals, thereby improving encoding efficiency while maintaining adequate prediction accuracy through motion compensation
2Productivity
If a blurring filter is applied to the reference frame, then encoding efficiency is improved, but decoder complexity increases due to additional filter parameters
Solution Approach 1:
The decoder infers the blurring filter parameters by analyzing the bitstream and motion compensated prediction samples itself, rather than requiring explicit parameter transmission. This self-service approach reduces overhead and minimizes decoder complexity while maintaining the benefits of filtered reference frames
3Loss of energy
If encoder filtering is applied to motion compensated prediction samples, then residual energy is reduced, but bitstream overhead increases due to filter parameter signaling
Solution Approach 1:
The encoder analyzes the effect of filtering on residual energy and uses this feedback to adaptively select filter parameters. The decoder mirrors this analysis to infer the same parameters, creating a feedback loop that optimizes residual energy reduction while minimizing the information that needs to be signaled in the bitstream
Data Source
AI summary
The present invention relates to a method of decoding a video bitstream, the method comprising the steps of: receiving a bitstream representing: residual samples produced by subtracting encoder filtered motion compensated prediction samples from image samples; and motion vectors used in forming the motion compensated prediction samples; the encoder filtering process conducted on the motion compensated prediction samples at an encoder having at least one parameter; using said motion vectors to provide motion compensated prediction samples from a previously reconstructed image; decoder filtering said motion compensated prediction samples in accordance with said at least one parameter; and adding said filtered motion compensated prediction samples to said residual samples to reconstruct images. A system and apparatus corresponding to this method are also disclosed.