Dyadic Spatial Re-sampling Filters for Scalable Video Coding

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Solution Overview

Problem

Existing dyadic spatial re-sampling filters in scalable video coding face challenges in balancing visual quality, power consumption, and memory usage, particularly due to complex phase calculations and additional low-pass filtering required for interpolation of sub-pixels in Extended Spatial Scalability (ESS) systems.

Innovation Solution

The implementation of dyadic spatial re-sampling filters using a Kaiser window function-based design, with specific tap values for down and up sampling filters, such as [−1, 17, 32, 17, −1]/64 for down sampling and [−5, 0, 21, 32, 21, 0, −5]/64 for up sampling, to simplify the filtering process and improve scalability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If Extended Spatial Scalability (ESS) is used for dyadic spatial re-sampling, then spatial scalability is improved, but phase calculations become complex and additional low-pass filtering is required

Engineering Contradiction:
Improvespatial scalabilityVSAvoidphase calculations complexity
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent extracts and eliminates the complex phase calculation component from the ESS re-sampling process by using a simplified filter bank approach. Instead of performing complex phase calculations for each sub-band, the invention uses a set of fixed filters that inherently handle the re-sampling operation, thereby reducing computational complexity while maintaining spatial scalability.

Inventive Principle:
Principle #2Taking out (Extraction)

Solution Approach 2:

The patent changes the parameters of the re-sampling process by using a fixed filter bank with specific tap values (e.g., [−1, 17, 32, 17, −1]/64) instead of variable phase calculations. This parameterization approach allows the system to achieve dyadic spatial scalability through simple filter application rather than complex adaptive phase rotation.

Inventive Principle:
Principle #35Parameter changes

2Manufacturing precision

If additional low-pass filtering is applied for sub-pixel interpolation in ESS, then interpolation accuracy is improved, but power consumption increases

Engineering Contradiction:
Improveinterpolation accuracyVSAvoidpower consumption
Core Design Contradiction:
Manufacturing precisionVSUse of energy by moving object

Solution Approach 1:

The patent merges the low-pass filtering operation with the re-sampling filter application. Instead of applying separate low-pass filtering stages for sub-pixel interpolation, the invention combines these operations into a single filter bank application, thereby reducing the total number of operations and power consumption while maintaining interpolation accuracy.

Inventive Principle:
Principle #5Merging (Combining)

Solution Approach 2:

The patent uses a partial filtering approach where the filter bank taps are designed to provide sufficient interpolation accuracy without requiring excessive filtering. The specific tap values (e.g., [−5, 0, 21, 32, 21, 0, −5]/64) are optimized to achieve the required precision with minimal computational effort, avoiding over-filtering and associated power costs.

Inventive Principle:
Principle #16Partial or excessive action

3Adaptability or versatility

If conventional re-sampling filters are used for dyadic spatial scalability, then compatibility with existing systems is maintained, but visual quality and coding efficiency are insufficient

Engineering Contradiction:
Improvesystem compatibilityVSAvoidvisual quality
Core Design Contradiction:
Adaptability or versatilityVSReliability

Solution Approach 1:

The patent changes the filter parameters to optimized values (e.g., [−1, 17, 32, 17, −1]/64 for downsampling and [−5, 0, 21, 32, 21, 0, −5]/64 for upsampling) that are specifically designed for dyadic spatial scalability. These parameter optimizations improve visual quality and coding efficiency while maintaining compatibility with the H.264/SVC framework through standard filter application procedures.

Inventive Principle:
Principle #35Parameter changes

Data Source

PatentUS8320460B2Dyadic spatial re-sampling filters for inter-layer texture predictions in scalable image processing
Publication Date: 2012.11.27 TAIWAN SEMICONDUCTOR MANUFACTURING CO LTD
  • US8320460B2 patent drawing
  • US8320460B2 patent drawing
  • US8320460B2 patent drawing

AI summary

A dyadic spatial down sampling filter having tap values configured according to a Kaiser window function a beta factor of approximately 2.5, having approximately 1.5 side lobes, and having a down sampling ratio of approximately 1.9. The dyadic spatial down sampling filter may have tap values [−1, 17, 32, 17, −1]/64. A dyadic spatial up sampling filter having tap values configured according to a Kaiser window function having a beta factor of approximately 1.5, having approximately 2 side lobes, and having an up sampling ratio of approximately 2. The dyadic spatial up sampling filter may have tap values [−5.44, 0, 20.71, 33.46, 20.71, 0, −5.44]/64.0, or tap values [−5, 0, 21, 32, 21, 0, −5]/64, or tap values [−5, 21, 21, −5]/32.