Motion and Texture Predictor Generation for Interlaced to Progressive Conversion

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Solution Overview

Problem

Existing hierarchical encoding methods with spatial scalability cannot generate motion and texture predictors for high resolution progressive sequences from low resolution interlaced sequences, as they rely on inter-layer prediction modes that are not applicable when the low resolution sequence is interlaced and the high resolution sequence is progressive.

Innovation Solution

A method is developed to generate motion and texture predictors for high resolution progressive pictures by subsampling motion and texture data from low resolution interlaced pictures, using modified Extended Spatial Scalability (ESS) methods, allowing for the creation of valid predictors even when the low resolution sequence is interlaced and the high resolution sequence is progressive, by adjusting inter-layer ratios and ensuring only valid motion vectors are used.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional inter-layer prediction modes are used to generate motion predictors for high resolution progressive pictures from low resolution interlaced pictures, then spatial scalability is achieved, but invalid motion vectors are generated due to mismatched temporal references and field/progressive incompatibility

Engineering Contradiction:

Improvevalidity of motion vectorsVSAvoidcompatibility between interlaced low resolution and progressive high resolution sequences

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent applies parameter changes by modifying the temporal reference parameters and field identification parameters when generating motion predictors. Specifically, it adjusts the temporal reference index and field identifier based on the progressive/interlaced mode and resolution level to ensure valid motion vector generation across different sequence types

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent introduces an intermediary mechanism that translates motion data from low resolution interlaced pictures to high resolution progressive pictures by adjusting temporal references and field identifiers. This intermediary process ensures compatibility between the two different sequence types without directly applying conventional prediction modes

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If motion data is subsampled from low resolution interlaced pictures to generate predictors for high resolution progressive pictures, then scalability is maintained, but complexity increases due to additional processing requirements

Engineering Contradiction:

Improveencoding efficiencyVSAvoidprocessing complexity for generating motion predictors

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the motion data processing into distinct steps: identifying the temporal reference, determining field mode, adjusting parameters, and generating predictors. This segmentation allows for systematic handling of the complexity while maintaining encoding efficiency

Inventive Principle:

Principle #1Segmentation

Data Source

PatentEP2005760B1Method of predicting motion and texture data

Publication Date: 2014.12.31 THOMSON LICENSING SA

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AI summary

The invention relates to a method for generating for at least one block of pixels of a picture of a sequence of progressive pictures at least one motion predictor and at least one texture predictor from motion data, respectively texture data, associated with the pictures of a sequence of low resolution interlaced pictures.