Colorization is the process of adding color to a monochrome image or movie. It is a challenging task because it requires segmenting images into regions and tracking these regions across frames, which is difficult to do automatically. This paper presents a simple colorization method that requires minimal user input. The method is based on the premise that neighboring pixels with similar intensities should have similar colors. The algorithm uses a quadratic cost function to minimize the difference between the color of a pixel and the weighted average of its neighbors. This leads to an optimization problem that can be solved efficiently using standard techniques. The user only needs to annotate the image with a few color scribbles, and the indicated colors are automatically propagated to produce a fully colorized image or sequence. The method is demonstrated on both still images and movie clips, showing that high-quality colorizations can be achieved with a relatively small amount of user input. The algorithm is based on the YUV color space, where Y represents the monochromatic luminance channel and U and V represent the chrominance channels. The algorithm works by minimizing the difference between the color of a pixel and the weighted average of its neighbors, using a weighting function based on the similarity of their intensities. The method is compared to other colorization techniques, showing that it produces better results in terms of color continuity and accuracy. The algorithm is also applicable to selective recoloring, which is useful in digital photography and special effects. The results show that the method can produce convincing colorizations with minimal user input, making it a promising approach for colorization. The paper also discusses the challenges of colorization, including the difficulty of segmenting images into regions and tracking these regions across frames. The method presented in this paper addresses these challenges by using a simple optimization approach that requires minimal user input. The algorithm is based on the premise that neighboring pixels with similar intensities should have similar colors, and it uses a quadratic cost function to minimize the difference between the color of a pixel and the weighted average of its neighbors. The method is demonstrated on both still images and movie clips, showing that high-quality colorizations can be achieved with a relatively small amount of user input. The algorithm is also applicable to selective recoloring, which is useful in digital photography and special effects. The paper concludes that the method presented is a promising approach for colorization, as it requires minimal user input and produces high-quality results.Colorization is the process of adding color to a monochrome image or movie. It is a challenging task because it requires segmenting images into regions and tracking these regions across frames, which is difficult to do automatically. This paper presents a simple colorization method that requires minimal user input. The method is based on the premise that neighboring pixels with similar intensities should have similar colors. The algorithm uses a quadratic cost function to minimize the difference between the color of a pixel and the weighted average of its neighbors. This leads to an optimization problem that can be solved efficiently using standard techniques. The user only needs to annotate the image with a few color scribbles, and the indicated colors are automatically propagated to produce a fully colorized image or sequence. The method is demonstrated on both still images and movie clips, showing that high-quality colorizations can be achieved with a relatively small amount of user input. The algorithm is based on the YUV color space, where Y represents the monochromatic luminance channel and U and V represent the chrominance channels. The algorithm works by minimizing the difference between the color of a pixel and the weighted average of its neighbors, using a weighting function based on the similarity of their intensities. The method is compared to other colorization techniques, showing that it produces better results in terms of color continuity and accuracy. The algorithm is also applicable to selective recoloring, which is useful in digital photography and special effects. The results show that the method can produce convincing colorizations with minimal user input, making it a promising approach for colorization. The paper also discusses the challenges of colorization, including the difficulty of segmenting images into regions and tracking these regions across frames. The method presented in this paper addresses these challenges by using a simple optimization approach that requires minimal user input. The algorithm is based on the premise that neighboring pixels with similar intensities should have similar colors, and it uses a quadratic cost function to minimize the difference between the color of a pixel and the weighted average of its neighbors. The method is demonstrated on both still images and movie clips, showing that high-quality colorizations can be achieved with a relatively small amount of user input. The algorithm is also applicable to selective recoloring, which is useful in digital photography and special effects. The paper concludes that the method presented is a promising approach for colorization, as it requires minimal user input and produces high-quality results.