Image colorization based on numerical modeling gives a highly accurate restoration result when colors are given to enough regions. A lot of numerical models focus on the relation between adjacent pixels; therefore, it is required to give the same color to various regions, and high spatial frequency regions are not colored properly. This letter proposes a colorization algorithm using graph signal processing. The key novelty of our algorithm is a new modeling method for a chrominance image using two different graphs. The first graph is a global graph, which connects the important pixels on an image. The second graph is a local graph, which connects the global graph and each pixel. Based on the hierarchical combination of these two graphs, color image is recovered. Numerical experiments show the effectiveness of the proposed algorithm by comparing with four existing methods.

Colorization-based image coding is a technique to compress chrominance information of an image using a colorization technique. The conventional algorithm applies graph Fourier transform to the colorization-based coding. In this algorithm, several pixels on the image are defined as vertices of the graph, and the chrominance values of that pixels are set as graph signals. Then, the graph signal corresponding to the several chrominance values on the image is transformed to the graph spectrum based on the graph Fourier transform, and the graph spectrum is compressed and stored. Because the stored graph spectrum gives the graph signal on the image based on the inverse graph Fourier transform in decoding phase, the color image is recovered from the luminance image and the several chrominance values corresponding to the graph signal. However, high calculation time is required to perform graph Fourier transform, and therefore, this paper proposes a fast graph Fourier transform to improve the conventional colorization-based image coding algorithm. In numerical examples, although the PSNR value is decreased 0.3 dB, the proposed algorithm is 16.8 times faster than the conventional method.

AYUMI, a mechanism that improves software quality and productivity and by which the development process is refined, is described. A series of methods which span activities from data evaluation to the feedback of findings has been developed. This methodology has approximately 100 evaluation items, including both quantitative and qualitative types. These items are categorized into about 40 groups and scored on a scale from one to five. The evaluation results are compiled into tables and reported to the project team. Work in the field is monitored, and feedback improves the development process. The operation of AYUMI is described, and some possible applications are discussed.>