Reduced Secondary Transform for Image Coding Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing demand for high-resolution and high-quality images/videos, such as 4K and 8K ultra high definition, leads to higher transmission and storage costs due to increased bit amounts, and existing compression techniques are inefficient for immersive media like virtual and augmented reality content.
Innovation Solution
The proposed solution involves an image coding method and apparatus that utilize a reduced secondary transform (RST) and a transform set to increase coding efficiency. This method includes deriving quantized transform coefficients, performing dequantization, applying an inverse RST based on a transform set determined by an intra prediction mode and a transform kernel matrix, and generating a reconstructed picture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image coding techniques are used for high-resolution images/videos, then image quality is maintained, but transmission and storage costs increase due to increased bit amounts
Solution Approach 1:
The patent segments the transform process into multiple stages: primary transform, secondary transform, and reduced secondary transform. This segmentation allows different transform types to be applied to different frequency components, achieving better compression efficiency while maintaining image quality. The transform coefficients are divided into multiple sets, with different secondary transforms applied to each set, enabling more efficient bit representation.
Solution Approach 2:
The patent changes the transform parameters adaptively based on the image content characteristics. Different secondary transform kernels are selected based on the primary transform type and block characteristics. The transform depth and precision are adjusted according to the frequency band, allowing optimal compression for each parameter range while maintaining overall image quality.
2Quantity of substance
If existing compression techniques are applied to immersive media, then some compression is achieved, but coding efficiency remains insufficient for virtual reality and augmented reality content
Solution Approach 1:
The patent introduces dynamic adaptivity in the transform process. The secondary transform type is dynamically selected based on the primary transform kernel and block characteristics. The transform parameters are adjusted in real-time according to the content being processed, enabling the system to adapt to different immersive media types (VR, AR, 360-degree video) and achieve optimal coding efficiency for each.
Solution Approach 2:
The patent creates a universal transform framework that can handle multiple types of immersive media content. The same transform structure supports different primary transforms (DCT, DST), different secondary transforms, and various block sizes. This multi-functional approach allows the system to efficiently compress diverse immersive media types including VR 360-degree video, AR content, and holographic data with a single unified codec.
3Measurement precision
If a full secondary transform is applied to all transform coefficients, then transform accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts and processes only the most significant transform coefficients through the reduced secondary transform. Instead of applying the full secondary transform to all coefficients, it identifies and processes only those coefficients that contribute most to image quality. This extraction approach maintains transform accuracy for critical components while eliminating unnecessary computational operations on less significant coefficients.
Solution Approach 2:
The patent applies partial action by using a reduced secondary transform that processes only a subset of transform coefficients rather than all coefficients. The transform is applied selectively to specific frequency bands and coefficient positions based on their importance. This partial application reduces computational complexity while maintaining sufficient accuracy for perceptual quality.
Data Source
AI summary
An image decoding method comprises the steps of: deriving transform coefficients through inverse quantization on the basis of quantized transform coefficients for a target block; deriving modified transform coefficients on the basis of an inverse reduced secondary transform (RST) for the transform coefficients; and generating a restoration picture on the basis of residual samples for the target block, on the basis of an inverse primary transform for the modified transform coefficients, wherein the inverse RST is performed on the basis of: transform sets determined by a mapping relation according to an intra prediction mode applied to the target block; and a transform kernel matrix selected from among two transform kernel matrices included in each of the transform sets, and is performed on the basis of: whether the inverse RST is applied; and a transform index for indicating any one of the transform kernel matrices included in the transform sets.