Secondary Transform Video Coding for Lower Bitrate Compression
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images/videos, including immersive media, leads to higher transmission and storage costs due to increased information bit amounts, necessitating a highly efficient image/video compression technique.
Innovation Solution
An image coding method and apparatus utilizing a reduced secondary transform (RST) with optimized transformation kernel matrices and transform sets based on intra prediction modes to enhance coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution and high-quality images/videos are transmitted or stored, then image quality and resolution are improved, but transmission cost and storage cost increase due to increased bit amount
Solution Approach 1:
The patent extracts and removes redundant information from images/videos through transform coding. By converting spatial domain data to frequency domain coefficients and applying quantization, the method extracts only the most significant visual information, discarding less important details that contribute to data redundancy. This extraction process reduces the bit amount while preserving perceived image quality.
Solution Approach 2:
The patent applies parameter changes through transform operations (e.g., DCT, DST) and quantization that convert image data from one representation to another. By changing the parameter representation from spatial pixel values to frequency coefficients and then to quantized coefficients, the method achieves more efficient compression. The secondary transform further optimizes this by applying additional parameter transformations to specific coefficient regions.
2Ease of manufacture
If conventional transform methods are used for image coding, then implementation is simple, but coding efficiency is insufficient for high-resolution images
Solution Approach 1:
The patent segments the transform process into multiple stages: primary transform, quantization, and secondary transform. The secondary transform is selectively applied to specific frequency bands or coefficient regions based on image characteristics and prediction modes. This segmentation allows the system to maintain simplicity for basic cases while achieving high coding efficiency when advanced transforms are beneficial.
Solution Approach 2:
The patent introduces dynamic adaptability by making the secondary transform application conditional based on various factors including prediction mode, block size, and image content characteristics. The transform parameters and kernel selection are dynamically adjusted according to the specific coding scenario, allowing the system to optimize between simplicity and efficiency based on real-time conditions.
Data Source
AI summary
A video decoding method according to the present document comprises the steps of: deriving transform coefficients by performing de-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) of the transform coefficients; and generating a reconstructed picture on the basis of residual samples for the target block on the basis of an inverse primary transform of the modified transform coefficients, wherein an inverse RST using a transform kernel matrix is performed on transform coefficients of an upper-left 4×4 region of an 8×8 region of the target block, and modified transform coefficients of the upper-left 4×4 region, an upper-right 4×4 region, and a lower-left 4×4 region of the 8×8 region are derived through the inverse RST.