Position Dependent Reference Sample Smoothing in Video Coding
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Solution Overview
Problem
Current video encoding and decoding systems face challenges in efficiently compressing and decompressing video sequences due to large data sizes, with existing technologies not adequately addressing the need for efficient storage and transmission while maintaining video quality.
Innovation Solution
The implementation of advanced video coding/decoding systems that utilize techniques such as intra and inter prediction, quadtree+multi-type tree partitioning, and adaptive interpolation filters to reduce data size by exploiting spatial and temporal redundancies in video sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If video data is compressed to reduce data size, then storage and transmission efficiency is improved, but video quality deteriorates
Solution Approach 1:
The patent applies different smoothing strengths to different spatial locations in the reference sample based on their distance from the current block. Samples closer to the current block receive stronger smoothing, while samples farther away receive weaker smoothing. This local adaptation allows the system to reduce data size through compression while preserving video quality in regions where it matters most.
Solution Approach 2:
The patent dynamically adjusts the smoothing strength parameter β based on the fractional sample position of the projection point. The smoothing strength varies continuously as the projection point moves along the reference sample line, allowing optimal balance between compression efficiency and quality preservation at each position.
2Manufacturing precision
If prediction accuracy is improved through adaptive filtering, then video quality is maintained, but processing complexity increases
Solution Approach 1:
The patent changes the smoothing strength parameter β as a function of the fractional sample position. By using a simple mathematical relationship between the fractional position and the smoothing parameter, the system achieves adaptive filtering without introducing complex processing algorithms, thus improving prediction accuracy while keeping computational complexity manageable.
3Measurement precision
If reference sample smoothing is applied, then prediction accuracy is improved, but computational time increases
Solution Approach 1:
The patent segments the reference sample line into discrete positions based on the fractional sample position of the projection point. By processing samples at these segmented positions with appropriate smoothing strengths, the system achieves accurate predictions while reducing the overall computational burden compared to uniform high-precision processing across all samples.
Data Source
AI summary
An encoder or decoder interpolates between a first reference sample and a second reference sample to determine an interpolated value at a point on a reference sample line to which a location of a sample is projected. The interpolating includes applying a first filter coefficient, determined based on a product of a first function and a second function that is inversely related to a distance of the point from the first reference sample, to the first reference sample and also applying a second filter coefficient, determined based on a product of the first function and a third function that is inversely related to a distance of the point from the second reference sample, to the second reference sample. The first function is inversely related to a distance of the point from a closest integer position on the reference sample line. The interpolated value is used to predict the sample.