2D Block Image Encoding Using Dynamic Quantization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image processing techniques face challenges in efficiently compressing and decompressing high-resolution image data for real-time transmission and display across various devices, such as smartphones and Blu-ray players, due to limitations in bit rate management and quantization step adjustments.
Innovation Solution
The implementation of a virtual buffer model in image encoders and decoders that dynamically adjusts the quantization step based on buffer fullness, allowing for efficient bit rate management and optimized compression, without explicitly transmitting quantization control information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional image compression techniques are used, then image data can be transmitted, but bandwidth usage is excessive and real-time transmission is difficult
Solution Approach 1:
The patent implements dynamic quantization step adjustment based on buffer fullness. The quantization step is not fixed but changes dynamically according to the buffer state, allowing the system to adapt to varying transmission conditions and achieve both bandwidth efficiency and real-time transmission capability.
Solution Approach 2:
The patent introduces a feedback mechanism where the encoder monitors buffer fullness and adjusts quantization parameters accordingly. This closed-loop control allows the system to respond to changing conditions in real-time, optimizing bandwidth usage while maintaining transmission timing requirements.
2Loss of energy
If quantization step is increased to improve compression, then bandwidth usage decreases, but image quality deteriorates
Solution Approach 1:
The quantization step is adjusted dynamically based on buffer fullness rather than being fixed or uniformly increased. This allows the system to use coarser quantization when bandwidth is constrained but maintain finer quantization when buffer conditions permit, thus preserving image quality while achieving bandwidth efficiency.
Solution Approach 2:
Different quantization steps are applied at different times based on local buffer conditions. The system applies appropriate quantization strength locally in time, matching the compression level to the immediate buffer state rather than applying a uniform quantization level throughout.
3Adaptability or versatility
If quantization control information is explicitly transmitted, then decoder can adjust quantization, but device complexity increases
Solution Approach 1:
The decoder independently determines quantization parameters by monitoring its own buffer fullness using the same algorithm as the encoder. This self-service approach eliminates the need for explicit quantization control information transmission, reducing device complexity while maintaining adaptability.
Solution Approach 2:
The buffer monitoring algorithm serves dual purposes: it controls encoder quantization and enables decoder quantization determination. This universal approach allows both encoder and decoder to operate independently with the same logic, eliminating the need for separate control signaling.
Data Source
AI summary
A coder (e.g., an encoder or decoder) implements coding of two dimensional blocks of image data using two dimensional differential pulse code modulation (2D DPCM). The coder may switch between DPCM and other types of coding, such as transform coding on a block by block basis. The 2D DPCM may obtain a reconstructed pixel within the two dimensional bloc and code a second, different, pixel within the two dimensional block using the reconstructed pixel. The coder may also create a bitstream of entropy encoded residuals that supports hybrid implicit/explicit specification of coding parameters.