Image Encoding Quantization Parameter Signaling
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Solution Overview
Problem
Existing codecs struggle to efficiently encode and decode high-resolution or high-definition image content, particularly in determining the optimal size and quantization parameters for coding units during image compression.
Innovation Solution
The method involves hierarchically splitting an image into coding units based on a split shape mode, obtaining a difference quantization parameter from a bitstream based on a predefined signaling unit, and using this parameter to calculate the quantization parameter for each coding unit, ultimately facilitating inverse-quantization and reconstruction of image blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image content is compressed using traditional codecs, then compression is achieved, but compression efficiency and image quality are insufficient for high-resolution content
Solution Approach 1:
The image is divided into multiple coding units of different sizes through hierarchical splitting. The encoder selects appropriate coding unit sizes (e.g., 64x64, 32x32, 16x16 blocks) for different regions of the image, allowing fine-grained control over compression in different areas while maintaining overall image quality.
Solution Approach 2:
Different quantization parameters are applied to different coding units based on their local characteristics. The difference quantization parameter signaling mechanism allows each coding unit to have optimized quantization strength, preserving important image details in critical regions while applying stronger compression in less important areas.
2Device complexity
If coding unit size is increased to reduce complexity, then processing complexity decreases, but quantization precision and image quality deteriorate
Solution Approach 1:
The image processing is segmented into multiple coding units of varying sizes. This allows the system to use larger coding units (reducing complexity) in suitable regions while using smaller coding units (maintaining precision) in regions requiring higher quality, thus balancing complexity and precision overall.
Solution Approach 2:
The quantization parameter is dynamically adjusted for each coding unit based on its size and local image characteristics. Larger coding units can use coarser quantization parameters while smaller units use finer parameters, optimizing the trade-off between complexity and precision for each region.
3Manufacturing precision
If difference quantization parameter signaling is implemented for all coding units, then quantization optimization improves, but bitstream complexity and signaling overhead increase
Solution Approach 1:
Difference quantization parameter signaling is applied selectively based on local needs. The encoder determines whether each coding unit requires explicit difference quantization parameter signaling or can inherit parameters from neighboring units, optimizing quantization where necessary while minimizing signaling overhead in regions where it is not needed.
Solution Approach 2:
Rather than signaling difference quantization parameters for all coding units, the method applies partial signaling only where beneficial. This partial action approach reduces bitstream overhead while still achieving quantization optimization in the most critical regions of the image.
4Adaptability or versatility
If hierarchical splitting is applied to all image regions, then compression adaptability improves, but processing time and computational load increase
Solution Approach 1:
Hierarchical splitting is applied to divide the image into coding units, but the depth and extent of splitting is adaptively controlled based on image content characteristics. This allows the encoder to achieve good compression adaptability in complex regions while limiting splitting in simple regions, thus reducing overall processing time.
Solution Approach 2:
Full hierarchical splitting is not applied uniformly to all image regions. Instead, the encoder performs partial hierarchical splitting only where it provides significant compression benefit, reducing computational load while maintaining adaptability where needed.
Data Source
AI summary
An image encoding and decoding method and apparatus are provided. The image decoding method includes obtaining a difference quantization parameter for a current coding unit from a bitstream, based on an area of a predefined difference quantization parameter signaling unit; obtaining a quantization parameter for the current coding unit by using a predicted quantization parameter for the current coding unit and the difference quantization parameter for the current coding unit; obtaining at least one inverse-quantized coefficient by performing inverse-quantization on at least one coefficient obtained based on residual information of the current coding unit included in the bitstream, based on the quantization parameter for the current coding unit; obtaining a residual block of the current coding unit, based on the inverse-quantized coefficient; and obtaining a reconstructed block of the current coding unit, based on the residual block of the current coding unit.