PATENT CLAIM ANALYSIS

Application Number: 16038154
Application Type: Utility
Filing Date: 2018-07
Publication Date: 2020-01
Patent Classification: ["375", "240020"]

Abstract:
A method based on CTU level rate-distortion optimization for rate control in video coding which can effectively improve the perceptual rate-distortion performance and coding efficiency is provided. Firstly, a perceptual rate-distortion model is established using a divisive normalization framework, which characterizes the relationship between local visual quality and coding bits. Subsequently, the established perceptual rate-distortion model is applied to overall distortion optimization which is transformed into a global optimization problem and solved with convex optimization algorithms to obtain optimal CTU level coding bit allocation.

Claim (Index 7):
An encoding method according to  claim 6 , wherein the optimization of R-D performance further comprises:\n adjusting the one or more optimal CTU level coding bit R* j  by: R j * = R j \u00d7 \u03c9 a \u03c9 a = ( 1 - \u2211 p = 1 j - 1 \ue89e ( R act , p - R p * ) R c ) , where \u03c9 a  is an adjustment term to regularize the CTU level coding bit to ensure the frame-level coding bit budget can be met, R act,p  and R* p  are the actual coding bits and the target coding bits after bit allocation, respectively.

Metadata:
- Claim Count in Document: 16.0
- Percentile: 95.0
- Lexical Diversity: 1.52542
- Patent Class: 375.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15352222', '16016691', '15260302', '12832495', '11530042']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4799441385636834
- 35 USC 102 Novelty (BERT): 0.536171946549377
- Combined Prediction Score: 0.4855669193622527
- Mean Citation Score: 279.06874200000004
- Max Citation Score: 346.58685
- Similarity Product: 225.4209696254969

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test