MS-SSIM Rate-Distortion Optimization Encoding System
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Solution Overview
Problem
Existing image compression systems face inefficiencies in real-time encoding due to high calculation loads associated with multi-scale structural similarity (MS-SSIM) calculations, which are resource-intensive and not suitable for real-time processing.
Innovation Solution
An encoding system that performs MS-SSIM-based rate-distortion optimization using a Lagrange multiplier, calculated based on MS-SSIM, to efficiently select an optimized prediction mode by reducing the computational load through mean squared error (MSE) calculations and modified Lagrange multiplier calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If MS-SSIM calculation is applied to evaluate image quality, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent divides the image into multiple scales or resolutions for MS-SSIM calculation, processing different frequency components separately. This segmentation allows the complex MS-SSIM metric to be computed in a structured manner that reduces overall computational burden while maintaining assessment accuracy.
Solution Approach 2:
The patent performs preliminary downsampling and preprocessing of images before applying the full MS-SSIM calculation. By preparing scaled versions of images in advance and computing certain components beforehand, the system reduces the real-time calculation load during actual rate-distortion optimization.
2Manufacturing precision
If MS-SSIM-based rate-distortion optimization is performed, then manufacturing precision is improved, but productivity deteriorates
Solution Approach 1:
The patent applies MS-SSIM-based rate-distortion optimization selectively to specific image regions or blocks rather than uniformly to the entire image. By applying the computationally intensive optimization only where needed (e.g., in complex regions or at critical decision points), the system maintains encoding quality while improving overall processing speed.
Solution Approach 2:
The patent dynamically adjusts parameters such as the number of scales used in MS-SSIM calculation, the block size for optimization, and the Lagrange multiplier values based on image content complexity and encoding conditions. This adaptive parameter adjustment allows the system to maintain high optimization accuracy when needed while reducing computational load for simpler cases, thereby improving real-time encoding performance.
Data Source
AI summary
An encoding system includes a prediction mode decision unit and an encoding unit. The prediction mode decision unit is configured to receive a target image and decide an optimized mode by applying rate-distortion optimization (RDO) to n (n is a natural number) modes of the target image. The encoding unit is configured to encode the target image according to the decided mode. The prediction mode decision unit includes a distortion calculation unit configured to calculate distortion by mean squared error (MSE) calculation between the target image and the n modes, a Lagrangian multiplier calculation unit configured to calculate a Lagrangian multiplier based on multi-scale structural similarity (MS-SSIM) calculation, and a bitrate calculation unit configured to calculate a bit rate of the n modes.