Image Encoding Pre-Filter for Wireless Transmission Robustness
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Solution Overview
Problem
Existing image encoding methods lack robustness in wireless transmission, leading to data losses and potential complete image loss due to transmission errors, especially in high error rates.
Innovation Solution
A method involving image segmentation into blocks, application of a pre-filter optimized using a representative image set, frequency-based transform, quantization, and binary encoding, along with error-resilient decoding processes to mitigate artefacts and enhance robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If standard image encoding methods (JPEG, etc.) are used to reduce data amount, then transmission speed improves, but robustness to transmission errors deteriorates
Solution Approach 1:
The image is divided into multiple blocks, and each block is processed independently through the encoding process. This segmentation allows errors to be localized to specific blocks rather than affecting the entire image, thereby maintaining robustness while enabling efficient compression and transmission of individual blocks
Solution Approach 2:
A pre-filter is applied to the image data before the main encoding process. This pre-filter is optimized using a training set of images to predict and mitigate artifacts that would otherwise be introduced by block-based processing and compression, thereby preserving image quality and robustness before transmission
2Quantity of substance
If block-based encoding is applied to reduce data size, then storage efficiency improves, but artefacts at block boundaries worsen
Solution Approach 1:
The pre-filter is applied before block segmentation and encoding to smooth transitions across block boundaries and reduce the discontinuities that cause visible artifacts. By addressing boundary issues before compression, the method maintains image continuity while achieving efficient data reduction
Solution Approach 2:
The pre-filter parameters are optimized through a training process using a set of representative images. This optimization adjusts filter characteristics to minimize artifact generation for specific image types or modalities, thereby reducing block boundary artifacts while maintaining compression efficiency
3Productivity
If compression is increased to improve transmission efficiency, then data transmission speed improves, but image quality deteriorates
Solution Approach 1:
The pre-filter prepares the image data by reducing high-frequency components and smoothing variations before compression. This preliminary processing allows for more aggressive compression with less visible quality loss, as the pre-filtered data contains fewer details that would otherwise be lost or distorted during compression
Solution Approach 2:
The method accepts that compression will introduce artifacts and quality loss, but uses the pre-filter to predict and mitigate these harmful effects. By anticipating and compensating for compression artifacts, the system converts the inevitable quality degradation into a manageable issue that can be pre-corrected
Data Source
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AI summary
A method for encoding data defining an image is disclosed. The method comprises segmenting the image into image blocks, each image block having a uniform block size, and applying a pre-filter, the pre-filter being applied to a group of pixels spanning a boundary between two image blocks. A frequency-based transform is applied to each of the image blocks, thereby providing transformed image data in which the image data is represented as coefficients defining a linear combination of predetermined basis functions having different spatial frequencies. The coefficients are quantised and converted into binary code. The pre-filter is determined at least in part by an optimisation process based on a set of selected images.