Invention Grant
- Patent Title: Compressing digital images utilizing deep learning-based perceptual similarity
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Application No.: US17032704Application Date: 2020-09-25
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Publication No.: US11335033B2Publication Date: 2022-05-17
- Inventor: Meet Patel , Mayur Hemani , Karanjeet Singh , Amit Gupta , Apoorva Gupta , Balaji Krishnamurthy
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Agency: Keller Preece PLLC
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06T9/00 ; G06T7/00 ; G06N3/08

Abstract:
Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing deep learning to intelligently determine compression settings for compressing a digital image. For instance, the disclosed system utilizes a neural network to generate predicted perceptual quality values for compression settings on a compression quality scale. The disclosed system fits the predicted compression distortions to a perceptual distortion characteristic curve for interpolating predicted perceptual quality values across the compression settings on the compression quality scale. Additionally, the disclosed system then performs a search over the predicted perceptual quality values for the compression settings along the compression quality scale to select a compression setting based on a perceptual quality threshold. The disclosed system generates a compressed digital image according to compression parameters for the selected compression setting.
Public/Granted literature
- US20220101564A1 COMPRESSING DIGITAL IMAGES UTILIZING DEEP LEARNING-BASED PERCEPTUAL SIMILARITY Public/Granted day:2022-03-31
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