Apple inc. (20240129472). CONTEXT MODELING IN ENTROPY CODING simplified abstract

CONTEXT MODELING IN ENTROPY CODING

Organization Name

apple inc.

Inventor(s)

Yeqing Wu of Cupertino CA (US)

Yunfei Zheng of Santa Clara CA (US)

Alican Nalci of Cupertino CA (US)

Yixin Du of Milpitas CA (US)

Hilmi Enes Egilmez of Santa Clara CA (US)

Guoxin Jin of San Diego CA (US)

Alexandros Tourapis of Los Gatos CA (US)

Jun Xin of San Jose CA (US)

Hsi-Jung Wu of San Jose CA (US)

CONTEXT MODELING IN ENTROPY CODING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240129472 titled 'CONTEXT MODELING IN ENTROPY CODING

Simplified Explanation

Improved Lossless Entropy Coding Techniques for Image Data

• Selecting context for entropy coding based on an ordered scan path of potential context locations within source images.
• Entropy coding a symbol for a current location in a source image based on prior encoded symbols of other locations, using the selected context.
• Context selection involves qualifying or disqualifying locations in the scan path to determine a predetermined number of prior symbols for encoding the current symbol.

Potential Applications: This technology can be applied in image compression algorithms, data transmission protocols, and storage systems where efficient lossless encoding of image data is required.

Problems Solved: 1. Improved compression efficiency for image data. 2. Enhanced data transmission speed and reduced storage requirements for image files.

Benefits: 1. Higher compression ratios leading to reduced storage space. 2. Faster data transmission due to smaller file sizes. 3. Improved image quality retention during compression and decompression processes.

Potential Commercial Applications of Improved Lossless Entropy Coding Techniques: Optimized for use in digital cameras, medical imaging devices, satellite imaging systems, and video streaming platforms.

Possible Prior Art: Previous lossless entropy coding techniques such as Huffman coding, Arithmetic coding, and Run-Length Encoding have been used in image compression applications.

Unanswered Questions: 1. How does this technology compare to existing state-of-the-art image compression methods in terms of compression ratios and processing speed? 2. Are there any limitations or constraints when applying these improved lossless entropy coding techniques to real-time image processing systems?

Original Abstract Submitted

improved lossless entropy coding techniques for coding of image data include selecting a context for entropy coding based on an ordered scan path of possible context locations. a symbol for a current location within a source image may be entropy coded based on a context of prior encoded symbols of other locations within source images, where the context is selected based on an ordered scan path enumerating a series of potential context locations within one or more source images. to select a context, a predetermined number of prior symbols may be selected by qualifying or disqualifying locations in the scan path, and then the current symbol may be encoded with a context based on prior symbols corresponding to the first qualifying context locations in the order of the scan path.