Video Encoder Reduces Complexity via Early Termination and Transform Skip
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Solution Overview
Problem
High Efficiency Video Coding (HEVC) encoders face increased encoding complexity, requiring significant computational resources and time, making it challenging to manage, store, and transmit digital image data efficiently.
Innovation Solution
The implementation reduces encoding complexity by dividing digital images into coding units, determining optimal prediction and transform units based on pixel values and bit requirements, and applying early termination and transform skip modes, thereby optimizing the coding structure and reducing computational intensity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If HEVC encoding is performed with high coding efficiency, then compression performance is improved, but encoding complexity increases significantly
Solution Approach 1:
The patent segments the encoding process into distinct modules: early termination decision module that divides coding units based on pixel value ranges, transform skip module that selectively applies transforms, and rate distortion optimization module that handles mode decisions. This segmentation allows each module to operate independently with reduced complexity while maintaining overall HEVC compression performance.
2Loss of information
If conventional HEVC encoding is performed, then coding efficiency is maintained, but encoding time is excessive
Solution Approach 1:
The patent performs preliminary actions by making early termination decisions before full encoding based on pixel value ranges, and by determining transform skip modes in advance using fast mode decisions. These preliminary decisions eliminate the need for computationally intensive processing in later stages, significantly reducing encoding time while preserving coding efficiency.
3Manufacturing precision
If full transform processing is applied to all units, then compression quality is improved, but computational intensity increases
Solution Approach 1:
The patent applies local quality by selectively applying transform processing only to specific transform units based on rate distortion optimization results, while applying transform skip mode to other units where it is not beneficial. This localized approach ensures compression quality is maintained where needed while reducing computational intensity in regions where full transform processing is unnecessary.
Data Source
AI summary
Some implementations can compress a digital image to create a compressed image corresponding to the digital image. The digital image can be divided into a plurality of coding units and a determination can be made whether to divide each coding unit of the plurality of coding units into smaller coding units/prediction units based on (i) a range of pixel values in each coding unit and/or (ii) a number of bits to encode the coding unit. Rate distortion optimization can be performed based on a size of each of the prediction units. A determination can be made whether to perform a transform for each of the transform units based on a size of each prediction unit and/or a spatial frequency measure associated with each transform unit.