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Adaptive Sharpening Filter Outlined in Intel Lunar Lake Xe2 Patch Notes

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Intel appears to working on an intriguing next generation adaptive sharpening filter—as revealed in mid-week published patch notes. Lunar Lake's display engine seems to be the lucky recipient here—its Xe2 "Battlemage" graphics architecture is expected to debut later this year. Second generation Intel Arc integrated graphics solutions have been linked to mobile Lunar Lake (LNL) processors—driver enablement was uncovered by Phoronix last September. The notes reveal that Team Blue is exploring a more intelligent approach to improving visual enhancements across games and productivity applications.

Author, Nemesa Garg (an engineer at Intel India) stated: "Many a times images are blurred or upscaled content is also not as crisp as original rendered image. Traditional sharpening techniques often apply a uniform level of enhancement across entire image, which sometimes result in over-sharpening of some areas and potential loss of natural details. Intel has come up with Display Engine based adaptive sharpening filter with minimal power and performance impact. From LNL onwards, the Display hardware can use one of the pipe scaler for adaptive sharpness filter. This can be used for both gaming and non-gaming use cases like photos, image viewing. It works on a region of pixels depending on the tap size."




Garg's description continued: "This RFC is an attempt to introduce an adaptive sharpness solution which helps in improving the image quality. For this new CRTC property is added. The user can set this property with desired sharpness strength value with 0-255. A value of 1 representing minimum sharpening strength and 255 representing maximum sharpness strength. A strength value of 0 means no sharpening or sharpening feature disabled. It works on a region of pixels depending on the tap size. The coefficients are used to generate an alpha value which is used to blend the sharpened image to original image. Userspace implementation for sharpening feature and IGT implementation is in progress."

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That's the one thing I like most about CMAA2 relative to FXAA it's more crisp and closer to native, but overhead is less ideal. I actually prefer layering FXAA and dialing back the relative strength of each and just using 1-3 configurations as needed and can either as A, B, or C together or just A and C or just B quite readily and easily since it's entirely balanced and scalable.
 
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