![]() Images such as JPEG represent 16,777,216 colors at 24 bits per pixel, and the difference from the original image can cause noticeable discomfort when converted to GIF.Ī process of hiding the discomfort caused by quantization error by adding artificial noise is sometimes implemented, called dithering. GIF converts the colors of the input pixels to 8-bit 256 or smaller, a process called quantization. On the other hand, in lossy compression, information is lost in the first stage, resulting in a loss of quality. The second stage, lossless compression, uses LZW encoding, which is a type of entropy encoding, similar to ZIP file compression without losing information easily. GIF images are created from raw images in two main steps: lossy compression and lossless compression. GIF Remastering creates results without edge noise, as shown in last image below, by applying a deep learning network algorithm that processes transparent background pixels to leave them unaffected. The opacity of the transparent background pixels is set to 0 so that they are practically invisible, but the RGB value information (mainly black 0) is still valid, so the RGB information of this transparent pixel affects the deep learning network and reduces noise. This is because deep learning networks also refer to the color of neighboring pixels when generating pixels at the edge of an object. If an image with a transparent background is passed through a deep learning network, unwanted noise will be generated at the pixels at the edge of the object, as shown in the 1st image below. ![]()
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