Video Encoding Subblock Rate-Distortion Optimization
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Solution Overview
Problem
Current video codecs are inadequate for efficiently encoding and decoding high-resolution or high-quality video content, as they rely on limited encoding methods based on fixed macroblock sizes, which hinder optimal compression and transmission.
Innovation Solution
The proposed solution involves a video encoding method that generates symbols through transformation and quantization based on prediction modes, updates probability indices of subblocks, determines rates and distortions, and calculates a rate-distortion (R-D) cost to optimize encoding decisions, allowing for parallel processing and adaptive encoding unit structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed macroblock size encoding is used, then device complexity is reduced, but compression efficiency deteriorates for high-resolution video
Solution Approach 1:
The encoding unit is divided into multiple subblocks, allowing independent processing and rate-distortion optimization for each subblock. This segmentation enables adaptive encoding strategies tailored to different regions of the video content, improving overall compression efficiency without significantly increasing system complexity.
Solution Approach 2:
The patent introduces dynamic selection of prediction modes and transformation block sizes based on rate-distortion cost calculations. The encoding parameters are adaptively adjusted for each subblock according to local content characteristics, transforming the static fixed-macroblock approach into a dynamic, content-adaptive system.
2Loss of substance
If adaptive encoding with multiple prediction modes is implemented, then compression efficiency is improved, but device complexity increases
Solution Approach 1:
The patent applies rate-distortion optimization selectively to subblocks rather than the entire macroblock. This partial application of complex algorithms to smaller, manageable units reduces the overall computational burden while still capturing the benefits of adaptive encoding in regions that require it.
Solution Approach 2:
The system dynamically changes encoding parameters such as prediction mode and transformation block size based on calculated rate-distortion costs for each subblock. This parameter adaptation allows the encoder to optimize compression efficiency without requiring a complete redesign of the encoding architecture.
3Productivity
If parallel processing is implemented for subblocks, then encoding speed is improved, but synchronization complexity increases
Solution Approach 1:
The macroblock is segmented into multiple subblocks that can be processed independently in parallel. Each subblock undergoes prediction, transformation, and rate-distortion calculation separately, enabling efficient parallelization while maintaining relatively simple synchronization requirements at the macroblock level.
Data Source
AI summary
An image encoding method includes generating symbols by performing transformation and quantization according to a transformation block, on a block that performs prediction according to a prediction mode; updating a probability index of a current sub block by using a probability index of a previous sub block among sub blocks included in the transformation block; determining a rate according to a bit length of the current sub block by using the probably index; determining a rate of the transformation block by using rates of the sub blocks; determining a distortion by using a difference between an original image and a reconstruction image according to transformation and quantization; and determining a rate-distortion (R-D) cost by using the distortion and the rate.