Pixel accuracy is a measure of how well a matting algorithm can correctly classify each pixel in an image as either foreground or background.

All of the above factors can affect pixel accuracy. A low-quality input image can make it difficult for the algorithm to accurately classify pixels. A complex scene with many objects and occlusions can also make it difficult for the algorithm to accurately classify pixels. And the algorithm itself can also affect pixel accuracy, with some algorithms being more accurate than others.

Intersection over Union (IoU) is the most common metric used to measure pixel accuracy. It is calculated by dividing the area of the intersection of the predicted and ground truth masks by the area of the union of the predicted and ground truth masks.

A good pixel accuracy score is typically above 90%. This means that the algorithm is correctly classifying at least 90% of the pixels in the image.

All of the above techniques can be used to improve pixel accuracy. Using a higher-quality input image can make it easier for the algorithm to accurately classify pixels. Using a more complex algorithm can also improve accuracy, but this can also increase the computational cost. And using a pre-trained model can also improve accuracy, as the model has already learned to classify pixels accurately.