Video Encoding Bitrate Optimization via Perceptual Quality Metrics

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing video compression technologies often fail to optimize encoding parameters based on the specific characteristics of the video content, leading to inefficiencies in bitrate allocation and quality tradeoffs, as they rely on default configurations that do not account for spatial complexity and motion variability.

Innovation Solution

The approach involves characterizing video content using perceptual quality metrics such as gradient magnitude similarity deviation (GMSD) and structural similarity (SSIM), combined with subjective testing to predict mean opinion scores (MOS), which are then used to determine adaptive bitrate settings that optimize both quality and bandwidth usage.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If default encoder configurations are used, then encoding simplicity is maintained, but video quality optimization based on content characteristics is lost

Engineering Contradiction:

Improvevideo qualityVSAvoidencoding process

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The system performs preliminary analysis of video content characteristics (spatial complexity, motion variability) before encoding to determine optimal encoder configurations. This advance characterization allows the encoder to be pre-configured with appropriate parameters for each video segment, avoiding the need for complex real-time adjustments during encoding while achieving quality optimization.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts encoder parameters (bitrate, resolution, encoding presets) based on measured video content characteristics. By changing these parameters according to the actual video properties rather than using fixed defaults, the system achieves optimized video quality while managing encoding complexity through automated parameter selection.

Inventive Principle:

Principle #35Parameter changes

2Loss of energy

If uniform bitrate is allocated to all video segments, then encoding simplicity is maintained, but bitrate waste in low-complexity regions occurs

Engineering Contradiction:

Improvebandwidth usageVSAvoidquality consistency

Core Design Contradiction:

Loss of energyVSManufacturing precision

Solution Approach 1:

The system applies different bitrate allocations to different video segments based on their local characteristics. Low-complexity regions receive lower bitrates while high-complexity regions receive higher bitrates, optimizing overall bandwidth usage without compromising quality where needed. This localized approach replaces uniform bitrate allocation with content-adaptive bitrate distribution.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system applies bitrate allocation selectively based on content needs rather than uniformly across all segments. By allocating bitrate partially (only where necessary) rather than excessively (uniformly everywhere), the system reduces overall bandwidth consumption while maintaining quality in critical regions through targeted bitrate application.

Inventive Principle:

Principle #16Partial or excessive action

3Manufacturing precision

If content-based adaptive encoding is implemented, then bitrate optimization is achieved, but measurement and characterization complexity increases

Engineering Contradiction:

Improvebitrate allocation accuracyVSAvoidvideo content analysis

Core Design Contradiction:

Manufacturing precisionVSDifficulty of detecting and measuring

Solution Approach 1:

The system divides video content into manageable segments and analyzes characteristics independently for each segment. This segmentation approach breaks down the complex task of analyzing entire videos into smaller, more tractable units, making content-based measurement feasible while achieving accurate bitrate allocation for each segment based on its specific characteristics.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11350105B2Selection of video quality metrics and models to optimize bitrate savings in video encoding applications

Publication Date: 2022.05.31 EUCLID DISCOVERIES LLC

AI summary

Videos may be characterized by objective metrics that quantify video quality. Embodiments are directed to target bitrate prediction methods in which one or more objective metrics may serve as inputs into a model that predicts a mean opinion score (MOS), a measure of perceptual quality, as a function of metric values. The model may be derived by generating training data through conducting subjective tests on a set of video encodings, obtaining MOS data from the subjective tests, and correlating the MOS data with metric measurements on the training data. The MOS predictions may be extended to predict the target (encoding) bitrate that achieves a desired MOS value. The target bitrate prediction methods may be applied to segments of a video. The methods may be made computationally faster by applying temporal subsampling. The methods may also be extended for adaptive bitrate (ABR) applications by applying scaling factors to predicted bitrates at one frame size to determine predicted bitrates at different frame sizes. A dynamic scaling algorithm may be used to determine predicted bitrates at different frame sizes.