Image Encoding with Slice-Based Bit Allocation for Error Resilience

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Solution Overview

Problem

Existing image encoding methods struggle to maintain high-quality image transmission while minimizing data requirements and ensuring resilience against data losses during wireless transmission.

Innovation Solution

The method involves segmenting images into uniform blocks, applying frequency-based transforms, grouping blocks into slices, and using fixed and variable length coding schemes to allocate bits, with error resilience enhanced by limiting bit allocation across a single slice and applying prediction techniques to coefficients.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If compression algorithms are applied to reduce data requirements, then transmission speed improves, but resilience to data corruption deteriorates

Engineering Contradiction:
Improvetransmission speedVSAvoidresilience to data corruption
Core Design Contradiction:
SpeedVSReliability

Solution Approach 1:

The image is divided into multiple blocks that are independently encoded and transmitted. This segmentation ensures that corruption in one block does not propagate to other blocks, maintaining resilience while allowing efficient compression of each block. The blocks can be transmitted separately and reconstructed independently at the receiver.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

Different encoding strategies are applied to different regions of the image based on their importance and characteristics. Critical regions receive more robust encoding with higher redundancy, while less critical regions use more aggressive compression. This local differentiation maintains overall image quality and resilience without sacrificing transmission efficiency.

Inventive Principle:
Principle #3Local quality

2Reliability

If error resilience is enhanced through multiple blocks and slices, then robustness to corruption improves, but processing complexity increases

Engineering Contradiction:
Improverobustness to corruptionVSAvoidprocessing complexity
Core Design Contradiction:
ReliabilityVSDevice complexity

Solution Approach 1:

The image is organized into a hierarchical structure of blocks within slices, creating manageable units for independent processing. This segmentation allows parallel processing of multiple blocks simultaneously, reducing overall processing complexity while maintaining robustness through the distributed structure.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

Blocks are pre-organized into slices with predetermined structures before encoding and transmission. This preliminary organization establishes error boundaries and processing pathways in advance, simplifying the decoding and error recovery processes without requiring complex real-time decision-making during transmission.

Inventive Principle:
Principle #10Preliminary action

3Ease of operation

If fixed length coding is used for zero-frequency coefficients, then decoding simplicity improves, but compression efficiency deteriorates

Engineering Contradiction:
Improvedecoding simplicityVSAvoiddata size
Core Design Contradiction:
Ease of operationVSQuantity of substance

Solution Approach 1:

Different coding schemes are selectively applied to different coefficient types based on their statistical properties and importance. Zero-frequency coefficients, which represent the average intensity and are crucial for image reconstruction, receive fixed-length coding for simplicity. Higher-frequency coefficients use variable-length coding for better compression, optimizing the overall balance between decoding simplicity and compression efficiency.

Inventive Principle:
Principle #3Local quality

Data Source

PatentUS20250386052A1Method for image encoding
Publication Date: 2025.12.18 MBDA UK
  • US20250386052A1 patent drawing
  • US20250386052A1 patent drawing
  • US20250386052A1 patent drawing

AI summary

A method for encoding data defining an image is disclosed. The method comprises segmenting the image into image blocks having a uniform size; and applying a frequency-based transform to each resulting in transformed image data in which the image data is represented as coefficients defining a linear combination of predetermined basis functions having different spatial frequencies, with each block of transformed image data having one coefficient for a zero frequency basis function, and a plurality of coefficients for higher frequency basis functions, which are grouped into one or more sub-bands. The blocks of transformed image data are grouped into slices, and the coefficients are converted into binary code using an allocation method to allocate bits associated with coefficients in each sub-band in a slice amongst-substantially same length bins. Bits associated with each sub-band in each slice are assigned to a bin and transferred, if needed, to achieve the substantially same length.