QualityScaler

QualityScaler is an AI video upscaler and local media processing tool designed to increase the resolution and visual quality of videos and images. It uses deep learning models to enhance detail and remove noise, operating as an offline application that executes all computations on local hardware.

The project functions as a GPU-accelerated media processor that distributes workloads across multiple graphics cards to increase rendering speed. To prevent memory overflow during high-resolution tasks, it employs a tiled image processing method that splits large assets into smaller sections.

The system includes a video frame interpolator to create smooth transitions between original and upscaled frames for improved visual clarity. It also features frame-level checkpointing to recover upscaling progress from the last successfully processed frame in the event of an interruption.

Features

Deep Learning Media Upscalers - Provides a deep learning media upscaler for increasing the resolution and quality of images and videos.

Tiled Resolution Scaling - Scales large images to higher resolutions while using tiling to prevent system memory exhaustion.

Resolution Enhancers - Uses pre-trained neural networks as resolution enhancers to increase pixel density and remove noise from media.

Local AI Image Enhancers - Performs heavy AI upscaling and enhancement tasks entirely on a local machine without internet connectivity.

Local Model Execution - Executes all model inference and media processing on the host hardware to operate without external network dependencies.

AI Upscaling - Increases the resolution and visual quality of videos using AI-driven upscaling models.

Local Media Processing - Executes all AI model inference and media processing on local hardware to operate without external network dependencies.

Hardware-Accelerated Media Processors - Operates as a hardware-accelerated media processor that distributes workloads across multiple GPUs.

Multi-GPU Distribution - Distributes computationally heavy media processing tasks across multiple graphics cards to increase rendering speed.

Multi-GPU Workload Distribution - Distributes computational workloads across multiple graphics cards to increase overall video and image rendering speed.

Media Processing Checkpoints - Provides frame-level checkpointing to resume interrupted upscaling jobs from the last successfully processed frame.

Image Tiling - Splits large images into smaller tiles during processing to prevent system memory exhaustion.

Tiled Upscaling - Employs tiled upscaling to split large assets into smaller sections and prevent GPU memory overflow.

Visual Enhancement Filters - Applies visual enhancement filters to reduce noise and increase clarity in upscaled media.

Motion-Based Frame Interpolation - Implements motion-based frame interpolation to blend original and upscaled frames for smoother transitions.

Processing Recovery States - Saves processing job progress to restart video upscaling from the last successfully processed frame.

Video Frame Interpolation Tools - Ships video frame interpolation tools to create smooth transitions between original and upscaled versions of a file.

DjdefragQualityScaler

Features

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