No abstract

Once a color image is converted to grayscale, it is a common belief that the original color cannot be fully restored, even with the state-of-the-art colorization methods. In this paper, we propose an innovative method to synthesize invertible grayscale. It is a grayscale image that can fully restore its original color. The key idea here is to encode the original color information into the synthesized grayscale, in a way that users cannot recognize any anomalies. We propose to learn and embed the color-encoding scheme via a convolutional neural network (CNN). It consists of an encoding network to convert a color image to grayscale, and a decoding network to invert the grayscale to color. We then design a loss function to ensure the trained network possesses three required properties: (a) color invertibility, (b) grayscale conformity, and (c) resistance to quantization error. We have conducted intensive quantitative experiments and user studies over a large amount of color images to validate the proposed method. Regardless of the genre and content of the color input, convincing results are obtained in all cases.

Adaptive coded modulation is a powerful method for achieving a high spectral efficiency over fading channels. Recently proposed adaptive schemes have employed set-partitioned trelliscoded modulation (TCM) and have adapted the number of uncoded bits on a given symbol based on the corresponding channel estimate. However, these adaptive TCM schemes will not perform well in systems where channel estimates are unreliable, since uncoded bits are not protected from unexpected fading. In this paper, adaptive bit-interleaved coded modulation (BICM) is introduced. Adaptive BICM schemes remove the need for parallel branches in the trellis-even when adapting the constellation size, thus making these schemes robust to errors made in the estimation of the current channel fading value. This motivates the design of adaptive BICM schemes, which will lead to adaptive systems that can support users with higher mobility than those considered in previous work. In such systems, numerical results demonstrate that the proposed schemes achieve a moderate bandwidth efficiency gain over previously proposed adaptive schemes and conventional (nonadaptive) schemes of similar complexity. Index Terms-Bit-interleaved coded modulation, time-varying fading channels, trellis-coded modulation.

This paper investigates optimal constellation labeling in the context of the edge profile. A constellation's edge profile lists the minimum-distance edge for each binary symbol error. The paper introduces the symmetric-ultracomposite (SU) labeling structure and shows that this structure provides undominated edge profiles for 2-PSK, 2-PAM, and 2 2-point square QAM. The SU structure is a generalization of the commonly used reflected binary Gray code. With the proper choice of basis vectors, SU labeling can support either set-partition or Gray-code labeling of 2-PSK, 2-PAM, and 2 2-point square QAM. Notably, there are Gray-code and set-partition labelings that do not have the SU structure. These labelings yield inferior edge profiles. The SU structure does not apply to cross constellations. However, for any standard cross constellation with 32 or more points, a quasi-SU labeling structure can approximate the SU structure. With the correct choice of basis, quasi-SU labelings produce quasi-Gray labelings. However, the quasi-SU structure cannot support set-partition labeling. In fact, the quasi-SU structure provides a better edge profile than standard set-partition labeling. Thus, for cross constellations there is a choice between edge profile optimality and the group structure provided by set-partitioning. Here, the correct choice depends on whether the encoder trellis has parallel branches.

Western color comics and Japanese-style screened manga are two popular comic styles. They mainly differ in the style of region-filling. However, the conversion between the two region-filling styles is very challenging, and manually done currently. In this paper, we identify that the major obstacle in the conversion between the two filling styles stems from the difference between the fundamental properties of screened region-filling and colored region-filling. To resolve this obstacle, we propose a screentone variational autoencoder, ScreenVAE, to map the screened manga to an intermediate domain. This intermediate domain can summarize local texture characteristics and is interpolative. With this domain, we effectively unify the properties of screening and color-filling, and ease the learning for bidirectional translation between screened manga and color comics. To carry out the bidirectional translation, we further propose a network to learn the translation between the intermediate domain and color comics. Our model can generate quality screened manga given a color comic, and generate color comic that retains the original screening intention by the bitonal manga artist. Several results are shown to demonstrate the effectiveness and convenience of the proposed method. We also demonstrate how the intermediate domain can assist other applications such as manga inpainting and photo-to-comic conversion.