Abstract: Camera tuning process is a time consuming and labor-intensive process. To address this issue, camera tuning system including a multi-modal large language model and a retrieval-augmented generation system can be implemented to intelligently and efficiently handle a camera tuning task in real-time. The multi-modal large language model can evaluate image quality and can be finetuned using high-quality labeled data and synthetically generated labeled data. The retrieval-augmented generation system can incorporate camera configuration knowledge into a vector database and can leverage a retrieved context to generate a configuration solution that addresses image quality issues identified by the multi-modal large language model. The resulting camera tuning system is a unified process that can identify image quality issues and provide configuration solutions that address both technical and aesthetic image quality concerns.

Abstract: A method for removing blocking artifacts from moving and still pictures, comprising classifying horizontal and vertical boundaries in each picture as blocky or non-blocky; for each blocky boundary, defining an adaptive, picture contentdependent, one-dimensional filtered pixels region of interest (ROI) that crosses the boundary and is bound at each of its ends by a bounding pixel; defining a finite filter having a length correlated with the length of the ROI; defining a filtering pixels expansion that uniquely determines the padding values of the finite length filtered pixels ROI for the finite length filtering; and filtering the ROI pixels using the finite filter and the filtering expansion.

Abstract: A method for removing blocking artifacts from moving and still pictures, comprising classifying horizontal and vertical boundaries in each picture as blocky or non-blocky; for each blocky boundary, defining an adaptive, picture contentdependent, one-dimensional filtered pixels region of interest (ROI) that crosses the boundary and is bound at each of its ends by a bounding pixel; defining a finite filter having a length correlated with the length of the ROI; defining a filtering pixels expansion that uniquely determines the padding values of the finite length filtered pixels ROI for the finite length filtering; and filtering the ROI pixels using the finite filter and the filtering expansion.

Abstract: A method for removing blocking artifacts from moving and still pictures, comprising classifying horizontal and vertical boundaries in each picture as blocky or non-blocky; for each blocky boundary, defining an adaptive, picture content-dependent, one-dimensional filtered pixels region of interest (ROI) that crosses the boundary and is bound at each of its ends by a bounding pixel; defining a finite filter having a length correlated with the length of the ROI; defining a filtering pixels expansion that uniquely determines the padding values of the finite length filtered pixels ROI for the finite length filtering; and filtering the ROI pixels using the finite filter and the filtering expansion.

Abstract: A method for removing blocking artifacts from moving and still pictures, comprising classifying horizontal and vertical boundaries in each picture as blocky or non-blocky; for each blocky boundary, defining an adaptive, picture content-dependent, one-dimensional filtered pixels region of interest (ROI) that crosses the boundary and is bound at each of its ends by a bounding pixel; defining a finite filter having a length correlated with the length of the ROI; defining a filtering pixels expansion that uniquely determines the padding values of the finite length filtered pixels ROI for the finite length filtering; and filtering the ROI pixels using the finite filter and the filtering expansion.