Tensor Quantization Masking for Region-Priority Compression
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Solution Overview
Problem
Existing content encoding systems face challenges in adaptively encoding regions of interest with high accuracy and computational efficiency, particularly in allocating different levels of information for various portions of content, such as images, during transmission or storage.
Innovation Solution
A compression system that adaptively quantizes tensor elements using a quantization mask and entropy coding, where each element is quantized based on its assigned alphabet size and probability values, allowing for variable compression levels and improved reconstruction quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If uniform quantization is applied to all tensor elements, then the encoding process is simple and computationally efficient, but the reconstruction quality of regions of interest is poor
Solution Approach 1:
The patent applies different quantization parameters (QP values) to different regions of the tensor based on a quantization mask. Regions of interest receive lower QP values (finer quantization) while non-critical regions receive higher QP values (coarser quantization), thereby achieving high reconstruction quality for important areas without uniformly complicating the entire encoding process
Solution Approach 2:
The tensor is divided into multiple regions with different quantization characteristics using a quantization mask. This segmentation allows independent optimization of quantization parameters for each region, enabling selective high-quality encoding of regions of interest while maintaining computational efficiency through region-based processing
2Measurement precision
If adaptive quantization is applied to prioritize regions of interest, then the reconstruction quality of important regions is improved, but the computational complexity increases
Solution Approach 1:
The quantization mask is generated in advance using a neural network that identifies regions of interest before the actual quantization process. This preliminary segmentation based on semantic understanding allows the subsequent quantization step to directly apply pre-determined QP values without complex real-time decision-making, thereby reducing computational complexity during encoding
Solution Approach 2:
A quantization mask serves as an intermediary data structure that bridges the gap between content importance assessment and quantization parameter application. The mask pre-identifies regions of interest and assigns appropriate QP values, simplifying the overall computational process by separating the complex task of region identification from the quantization operation itself
3Productivity
If different alphabet sizes are used for different tensor elements, then the compression efficiency for regions of interest is improved, but the encoding process becomes more complex
Solution Approach 1:
The patent dynamically changes the alphabet size parameter for different tensor elements based on their importance as indicated by the quantization mask. Critical regions use larger alphabets (more quantization levels) for better precision, while non-critical regions use smaller alphabets for higher compression efficiency. The QP values serve as controllable parameters that regulate this alphabet size variation
Solution Approach 2:
The quantization system dynamically adapts the alphabet size and QP values for different regions rather than using fixed parameters throughout. This dynamic adjustment allows the encoding process to optimize compression efficiency for each region based on its importance, with the neural network-guided quantization mask providing real-time guidance on parameter selection
Data Source
AI summary
A compression system includes an encoder and a decoder. The encoder can be deployed by a sender system to encode a tensor for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode and reconstruct the encoded tensor. The encoder receives a tensor for compression. The encoder also receives a quantization mask and probability data associated with the tensor. Each element of the tensor is quantized using an alphabet size allocated to that element by the quantization mask data. The encoder compresses the tensor by entropy coding each element using the probability data and alphabet size associated with the element. The decoder receives the quantization mask data, the probability data, and the compressed tensor data. The quantization mask and probabilities are used to entropy decode and subsequently reconstruct the tensor.