ADAPTIVE REAL-TIME ISP HARMONIZATION FOR BACKGROUND REPLACEMENT AND CONTENT EDITING

Smart overview of the Invention

Systems and methods are designed to address the visual inconsistencies that arise when a foreground subject is composited onto a different background. This often occurs in video conferencing and content creation, where lighting conditions and image processing histories differ between the original and replacement backgrounds. Conventional methods rely on post-processing techniques that adjust the foreground's visual characteristics to better match the new background, but these approaches are computationally intensive and can introduce artifacts such as banding and posterization. The proposed solution integrates harmonization directly within the Image Signal Processor (ISP) hardware pipeline, reducing computational overhead and preserving image quality.

Harmonization Process

The harmonization process begins by receiving a sensor image that includes both foreground and background regions, along with a separate background image. These images are downscaled and processed by a harmonization neural network that predicts parameters for ISP hardware blocks. The predicted parameters enable the ISP to harmonize the foreground region to match the background's visual characteristics, while maintaining full sensor bit depth. This harmonized image is then blended with the background image to create a seamless composition. By performing harmonization within the ISP using low-resolution inference, the system minimizes computational demands and avoids the artifacts associated with post-processing.

Neural Network Integration

The harmonization neural network is integrated into the ISP's existing architecture, utilizing a parallel, low-resolution processing path. A background/foreground segmentation AI model identifies which parts of the scene are foreground and which are background, providing a segmentation mask as input to the neural network. The network extracts embeddings from the downscaled images and predicts hardware configuration parameters for the ISP blocks. These parameters include tone mapping correction curves and color mapping matrices, which adjust the foreground's color temperature, brightness, and contrast to match the background's lighting context.

Preservation of Image Quality

Harmonization within the ISP pipeline preserves full sensor bit depth throughout processing, avoiding the quantization artifacts that occur with 8-bit post-processing systems. The ISP sensors capture data at higher bit depths, which is maintained during harmonization to prevent degradation artifacts. Additionally, a blend parameter control mechanism allows for adjustable harmonization strength, ensuring that skin tones and other sensitive areas remain natural. This mechanism uses the ISP's face detection and skin tone statistics to automatically set the blending factor, preventing over- or under-harmonization.

Training and Efficiency

The harmonization neural network is trained using a supervised learning approach with synthetically generated image pairs. Real photographic scenes serve as ground truth, while non-harmonized composite inputs are created by applying color transfer functions to foreground regions. The network learns to predict ISP parameters that map these inputs back to the ground truth. To enhance efficiency, harmonization inference is decoupled from the video frame rate, leveraging the ISP's scene-change detection to trigger re-determination only when necessary. This approach reduces power consumption and maintains temporal stability in video applications.