Image Quantization Parameter Estimation via Frequency Distribution Sampling
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Solution Overview
Problem
Existing image compression methods, such as block transform coding, are computationally intensive and require significant memory resources to estimate original quantization parameter values, making them unsuitable for resource-constrained environments like embedded systems.
Innovation Solution
The method involves dividing an image into blocks, generating frequency domain vectors, determining preliminary estimates of quantization parameter values from frequency distributions, and extrapolating these values using a small sample of image blocks and reference sets, reducing computational and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If block transform coding with histogram analysis and optimization processes is used to estimate quantization parameter values, then measurement precision of quantization parameters is improved, but use of energy and device complexity increase significantly
Solution Approach 1:
The patent segments the image into multiple blocks and processes them independently to estimate quantization parameters. By dividing the image into smaller units (e.g., 8x8 pixel blocks), the computational workload is distributed across multiple smaller processing tasks rather than analyzing the entire image as one large unit, reducing overall energy consumption while maintaining estimation accuracy through statistical aggregation across blocks.
Solution Approach 2:
The patent applies partial action by using a simplified estimation method that processes only a subset of transform coefficient data rather than performing complete histogram analysis on all coefficients. The method uses preliminary estimates from frequency distributions and applies optimization only where necessary, avoiding exhaustive computation while achieving sufficient precision for practical purposes.
2Measurement precision
If histogram analysis of forward transform coefficients is performed to estimate quantization parameter values, then measurement precision is improved, but device complexity and memory resources increase
Solution Approach 1:
The patent extracts only the essential features needed for quantization parameter estimation by focusing on frequency domain vector analysis and key statistical properties rather than performing complete histogram analysis. It extracts preliminary estimates from frequency distributions and uses these as sufficient inputs, removing the need for complex optimization processes and extensive memory storage of coefficient histograms.
Solution Approach 2:
The patent uses disposable, lightweight data structures for temporary storage of frequency domain vectors and preliminary estimates during processing. Rather than maintaining complex histogram data structures in memory, it uses simple arrays and counters that can be quickly allocated and discarded for each image block, reducing memory requirements and device complexity.
3Measurement precision
If optimization processes are performed to evaluate likelihood functions for quantization parameter estimation, then measurement precision is improved, but productivity decreases due to computational intensity
Solution Approach 1:
The patent performs preliminary action by computing frequency domain vectors and preliminary quantization parameter estimates before applying any optimization processes. These preliminary estimates serve as excellent initial guesses that are already close to the final optimal values, meaning that if optimization is applied, it requires far fewer iterations to converge, thereby maintaining precision while improving processing throughput.
Solution Approach 2:
The patent implements periodic action by applying optimization processes only to selected image blocks or at specific intervals rather than continuously optimizing every block. It processes blocks in batches, performing optimization periodically on representative samples and using these results to guide subsequent processing, which maintains overall estimation accuracy while significantly improving processing throughput.
Data Source
AI summary
Methods, machines, systems and machine-readable instructions for processing an image are described. In one aspect, the image is divided into a population of image blocks. Frequency domain vectors are generated from respective ones of the image blocks. Each of the frequency domain vectors includes a respective set of values corresponding to a set of transform coefficients. Preliminary estimates of quantization parameter values are determined from frequency distributions of the transform coefficient values in a set of the frequency domain vectors corresponding to a variable sample of the population. The variable sample is determined at least in part by at least one threshold. Values of quantization parameters are estimated from the preliminary estimates.