Superpixel Border Chain Coding for Lower-Bitrate Image Compression
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Solution Overview
Problem
Existing image and video coding techniques face challenges in efficiently encoding and decoding images with discontinuities, leading to reconstruction artifacts and increased bitrate due to the predefined square shape of blocks in Discrete Cosine Transform (DCT) methods, which are not well-suited for images with complex boundaries.
Innovation Solution
A new chain coding strategy that encodes superpixels borders without the need for a starting reference vertex, using symbols that produce a skewed probability distribution to optimize entropy coding, allowing for efficient representation and compression of image contours.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If DCT with predefined square blocks is used for image coding, then the encoding process is simple and computationally efficient, but reconstruction artifacts increase and compression efficiency decreases when images contain discontinuities or complex boundaries
Solution Approach 1:
The image is segmented into superpixels using the SLIC algorithm, which divides the image into semantically homogeneous regions that adapt to image content rather than using fixed square blocks. This segmentation allows the coding to follow natural boundaries and discontinuities in the image, reducing reconstruction artifacts while maintaining encoding efficiency through the structured superpixel representation.
2Ease of operation
If standard chain coding is used to encode superpixel borders, then the encoding process is straightforward, but the number of symbols increases and compression efficiency decreases due to uniform symbol probability distribution
Solution Approach 1:
The patent introduces an asymmetric chain coding scheme where the first vertex of each superpixel border is encoded differently from subsequent vertices. The first vertex uses a reference to a previously encoded vertex (or image corner), while subsequent vertices use differential coding relative to their predecessors. This asymmetric approach creates a skewed symbol probability distribution that enables more efficient entropy coding, reducing the total number of symbols needed while maintaining encoding simplicity.
Data Source
Figure 1(a)~1(b)
Figure 2
Figure 3(a)~3(b)
AI summary
The present invention relates to a method for encoding the borders of pixel regions of an image, wherein the borders contain a sequence of vertices subdividing the image into regions of pixels (superpixels), by generating a sequence of symbols from an alphabet comprising the step of: - defining for each superpixel a first vertex for coding the borders of the superpixel according to a criterion common to all superpixels; - defining for each superpixel the same coding order of the border vertices, either clockwise or counter-clockwise; - defining the order for coding the superpixels on the base of a common rule depending on the relative positions of the first vertices; - defining a set of vertices as a known border (Γ), wherein the following steps are performed for selecting a symbol of the alphabet, for encoding the borders of the superpixels: a) determining the first vertex of the next superpixel border individuated by the common criterion; b) determining the next vertex to be encoded on the basis of the coding direction; c) selecting a first symbol ("0") for encoding the next vertex if the next vertex of a border pertains to the known border (Γ), d) selecting a symbol ("1"; "2") different from the first symbol ("0") if the next vertex is not in the known border ((Γ); e) repeating steps b), c), d) and e) until all vertices of the superpixel border have been encoded; f) adding each vertex of the superpixel border that was not in the known border (Γ) to the set ((Γ); g) determining the next superpixel whose border is to be encoded according to the common rule, if any; i) repeating steps a)-g) until the borders of all the superpixels of the image have being added to the known border (Γ).