Trellis Quantizer Rate-Distortion Optimization Video Compression
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Solution Overview
Problem
Current rate-distortion optimization (RDO) techniques for compressing motion video require significant processing and storage resources, leading to increased power consumption and delays, especially in portable devices, due to complex calculations and the need for feedback-based bitcost determination.
Innovation Solution
Implementing a trellis quantizer that compares overall cost values of coefficient values and alternate values, using estimates based on a context model for bitcost calculations and values in the frequency domain for distortion calculations, and performing these determinations in separate hardware/software pipelines to reduce resource requirements, while skipping calculations for zero coefficient values and making the rounding value programmable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If rate-distortion optimization (RDO) techniques are used to account for bitcosts and distortion in quantization, then compression efficiency is improved, but processing and storage resource requirements increase significantly
Solution Approach 1:
The patent segments the RDO process into separate hardware and software pipelines. The hardware pipeline performs parallel calculations of bitcost and distortion metrics for multiple quantization parameters simultaneously, while the software pipeline handles coefficient processing and entropy encoding. This segmentation allows complex RDO calculations to be distributed across different processing domains, improving compression efficiency without overwhelming single processing units.
Solution Approach 2:
The patent performs preliminary calculation of bitcost estimates using context models before final quantization decisions are made. By pre-calculating probability models and bitcost metrics for different quantization parameter combinations, the system prepares optimization data in advance, allowing faster selection of optimal parameters during actual compression without requiring extensive real-time computation.
2Manufacturing precision
If feedback-based bitcost calculations are used in RDO, then compression accuracy is improved, but delays in video compression increase
Solution Approach 1:
The patent calculates bitcost estimates using context models in advance, before actual encoding occurs. These preliminary bitcost calculations provide accurate enough guidance for quantization parameter selection without waiting for complete feedback from the encoding process. This eliminates the feedback delay while maintaining sufficient compression accuracy.
Solution Approach 2:
The patent uses lightweight context models that provide approximate bitcost estimates rather than precise feedback-based calculations. These simplified models consume minimal processing resources and produce sufficiently accurate results for real-time compression, replacing the need for expensive, time-consuming feedback loops.
3Manufacturing precision
If complex RDO calculations are performed, then quantization optimization is improved, but electric power requirements increase
Solution Approach 1:
The patent divides complex RDO calculations into separate hardware and software components that can operate in parallel. The hardware pipeline handles computationally intensive bitcost and distortion calculations using dedicated circuits that consume less power than general-purpose processors. This segmentation allows optimized quantization to be achieved with lower overall power consumption by matching computational tasks to appropriate processing platforms.
Solution Approach 2:
The patent replaces software-based RDO calculations with hardware-implemented pipelines that perform the same mathematical operations more efficiently. By implementing quantization optimization logic in hardware circuits rather than software routines, the system achieves the same quantization optimization with reduced power consumption, as hardware operations are more energy-efficient for repetitive computational tasks.
Data Source
AI summary
Various embodiments are generally directed to techniques for reducing processing and/or storage resource requirements for RDO in compressing motion video. A device to compress motion video includes a first cost calculator to derive a first bitcost value of using a quantized coefficient of a quantized coefficient block of a frame of a video in compressing the video, the first bitcost value based on a context model of a context-adaptive binary arithmetic coder (CABAC); and a second cost calculator to derive a second bitcost value of replacing the quantized coefficient with an alternate value derived from the quantized coefficient in compressing the video, the second bitcost value based on the context model. Other embodiments are described and claimed.