autogressive pixel prediction in the neighbourhood of image borders

Simple SummaryContent extracted from patent full text and abstract with AI.

This invention provides an advanced method for compressing digital images by improving the prediction of pixel values near the borders of an image. The method adapts autoregressive pixel-prediction (where a pixel's value is estimated using nearby reconstructed pixels) by intelligently adjusting the neighborhood and training regions, especially at image edges where less data is available. By iteratively pruning these regions and adjusting weights based on available reconstructed pixels, the method maintains high prediction accuracy, resulting in more efficient image coding and compression.

Use CasesContent extracted from patent full text and abstract with AI.

• Video compression algorithms compliant with H.264/AVC, HEVC, or similar standards, to improve compression near image/block edges.
• Efficient storage and transmission of medical images (such as CT or MRI volumes) where high-fidelity image data is required, particularly in multi-dimensional datasets.
• Image and video archival systems needing high-compression with lossless or near-lossless quality.
• Real-time video streaming applications where efficient parallel processing and rapid decoding are essential.
• Image restoration, denoising, and error concealment in cases where border regions are prone to data loss or noise.

BenefitsContent extracted from patent full text and abstract with AI.

• Significantly improves prediction accuracy at the borders of images, reducing typical errors found in prior methods.
• Enables efficient and robust image compression without the need for artificial padding or complex border treatments.
• Supports both lossless and lossy compression modes.
• Facilitates highly parallelized, fast image and video encoding/decoding, beneficial for modern hardware architectures (e.g., multi-core or SIMD processors).
• Reduces overall prediction error, leading to better compression ratios and potentially lower storage or transmission costs.
• Can be pre-computed and stored to minimize computational cost during actual encoding/decoding, making it suitable for real-time applications.
• Adaptable to various image structures, block sizes, and multidimensional image data (such as 2D, 3D, or even hyperspectral images).

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Inventors & Applicants

Patent Abstract

The Invention refers to a method for coding digital image data (I), the data comprising one or more arrays of pixels (p0, ..., p112) with corresponding pixel values (i0, ..., i112). For each array of pixels, an autoregressive pixel-prediction method is performed based on a weighted sum of reconstructed pixel values (i1, ..., i12) of reconstructed pixels (p1, ..., p12) in a specific neighborhood region (SN) adjacent to the current pixel (p0) to be coded. For determining the weights, the pixel values (p1, ..., p12) in a specific training region (ST) adjacent to the current pixel (p0) are taken into account. The coding method is characterized by an appropriate determination of the specific neighborhood region (SN) and the specific training region (ST) in case that reconstructed pixel values do not exist for all pixels in the neighborhood region and the training region, e.g. at borders of the array of pixels. In such a case, the number of pixels in the neighborhood region is reduced to a number of reconstructed pixels until the ratio between the number of pixels in the training region and the number of pixels in the neighborhood region exceeds a predetermined threshold. The coding method of the invention provides a prediction with high accuracy and, thus, leads to an efficient coding of the image data.