Blockwise Image Quality Control in Autoencoder Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional encoders produce compressed images with constant quality at each bit-rate point, lacking the ability to adaptively vary image quality in the spatial domain during inference and compression, and do not provide detailed methods for implementing adaptive Quantization Parameter (QP) algorithms.
Innovation Solution
Incorporating a QP matrix into convolutional layers of an autoencoder system, where lambda values associated with each subblock of QP values are varied to modify activation outputs, allowing adaptive quality control and enabling bitstream adaptation to dynamic network bandwidth changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single autoencoder model with fixed lambda value is used for image compression, then the system complexity is reduced and training is simplified, but the ability to adaptively control image quality in different spatial regions is lost
Solution Approach 1:
The image is divided into multiple blocks, and each block is assigned a different lambda value from a QP matrix. This segmentation allows different quality levels to be applied to different spatial regions, enabling adaptive quality control without requiring multiple complete autoencoder models.
Solution Approach 2:
Different lambda values are applied to different blocks of the image based on local characteristics such as activity level or importance. This local quality approach allows the system to allocate bits more efficiently by applying higher quality compression to important regions and lower quality to less important regions.
2Manufacturing precision
If adaptive QP is applied in conventional encoders to vary quality in different spatial regions, then image quality is improved, but the encoder complexity increases and detailed implementation methods are not standardized
Solution Approach 1:
The adaptive QP functionality is merged into the autoencoder framework by incorporating a QP matrix that provides lambda values for different blocks. This combines the advantages of deep learning-based compression with traditional adaptive QP techniques, achieving improved image quality without significantly increasing encoder complexity.
Solution Approach 2:
The lambda values in the QP matrix can be dynamically adjusted based on block characteristics such as activity level, importance, or network conditions. This dynamic adaptation allows the system to optimize image quality for different content types and transmission conditions without requiring complex retraining.
3Adaptability or versatility
If multiple trained NN systems are used to achieve different bit rates and quality levels, then adaptability to different network conditions is improved, but energy consumption and system complexity increase
Solution Approach 1:
A single autoencoder model is trained to handle multiple bit rates and quality levels by incorporating a QP matrix with different lambda values. This universal model can adapt to different network conditions and quality requirements without requiring separate trained models, thereby reducing energy consumption and system complexity.
Solution Approach 2:
Instead of changing the model structure or retraining for different bit rates, the system changes the lambda parameters in the QP matrix. This parameter-based adaptation allows the single model to produce different quality outputs by simply adjusting the lambda values associated with different blocks, avoiding the energy cost of multiple trained systems.
Data Source
AI summary
A method for encoding or decoding an image is provided. The method comprises obtaining pixel values of pixels included in the image. The method further comprises converting the pixel values into convoluted values using a convolution network that comprises a first convolution layer, wherein the first convolution layer is configured to receive first input values and generate first output values using a convolution operation. The method further comprises obtaining first quality values, and (i) combining the first quality values with the pixel values, thereby generating the first input values or (ii) combining the first quality values with the first output values, thereby generating first combined values.