17915579. ADAPTIVE LOOP FILTER (ALF) VIRTUAL BOUNDRY PROCESSING simplified abstract (Telefonaktiebolaget LM Ericsson (publ))

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ADAPTIVE LOOP FILTER (ALF) VIRTUAL BOUNDRY PROCESSING

Organization Name

Telefonaktiebolaget LM Ericsson (publ)

Inventor(s)

Kenneth Andersson of Gävle (SE)

[[:Category:Jacob Str�m of Stockholm (SE)|Jacob Str�m of Stockholm (SE)]][[Category:Jacob Str�m of Stockholm (SE)]]

Zhi Zhang of Solna (SE)

Jack Enhorn of Kista (SE)

ADAPTIVE LOOP FILTER (ALF) VIRTUAL BOUNDRY PROCESSING - A simplified explanation of the abstract

This abstract first appeared for US patent application 17915579 titled 'ADAPTIVE LOOP FILTER (ALF) VIRTUAL BOUNDRY PROCESSING

Simplified Explanation

The abstract describes a method for processing an image by obtaining a set of sample values associated with the image. The set of sample values includes a current sample value and neighboring sample values within a rectangular block. The current sample value has a vertical position, y. The method involves selecting a filter strength value based on y, where a first filter strength value is selected if a condition is satisfied, and a second filter strength value is selected if the condition is not satisfied. The condition is satisfied only when y is equal to a predetermined value.

  • The method involves processing an image by obtaining a set of sample values and selecting a filter strength value based on the vertical position of the current sample value.
  • The filter strength value is determined based on whether a condition is satisfied, which depends on the vertical position of the current sample value.
  • The current sample value is then filtered based on the selected filter strength value.

Potential applications of this technology:

  • Image processing and enhancement: The method can be used to improve the quality and clarity of images by applying appropriate filters based on the vertical position of the sample values.
  • Computer vision: The method can be utilized in computer vision applications, such as object recognition and tracking, to enhance image features and reduce noise.
  • Medical imaging: The method can be applied to medical images to enhance details and improve diagnostic accuracy.

Problems solved by this technology:

  • Inconsistent filtering: By selecting the filter strength value based on the vertical position of the sample values, the method ensures that the appropriate filter is applied consistently across the image.
  • Noise reduction: The method helps in reducing noise in images by applying stronger filters to areas with higher noise levels, based on the vertical position of the sample values.

Benefits of this technology:

  • Improved image quality: The method enhances the quality of images by applying filters tailored to the specific vertical position of the sample values.
  • Noise reduction: By selecting the appropriate filter strength value, the method effectively reduces noise in images, resulting in clearer and more accurate representations.
  • Versatility: The method can be applied to various image processing tasks and is adaptable to different vertical positions, allowing for flexibility in different applications.


Original Abstract Submitted

A method () for processing an image. The method includes obtaining (s) a set of sample values associated with the image, the set of sample values comprising a current sample value and sample values neighboring the current sample value, wherein the obtained set of sample values is included within a rectangular block of sample values, and the current sample value has a vertical position, y. The method also includes selecting (s) a filter strength value based on y, wherein selecting the filter strength value based on y comprises: selecting a first filter strength value if a condition is satisfied, otherwise if the condition is not satisfied selecting a second filter strength value, wherein the condition is satisfied only when y is equal to a predetermined value and the condition is not satisfied if y is not equal to the predetermined value. The method also includes filtering (s) the current sample based on the selected filter strength value.

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