Chroma intra prediction aims to reduce chroma redundancies within a frame, which plays an important role in improving the coding efficiency of intra coding. Existing chroma intra prediction methods typically utilize the spatial relationship between the current luma block and its neighboring reference luma blocks to predict its chroma samples. However, the spatial properties of luma components differ from those of chroma components, which limits the accuracy of chroma intra prediction. To tackle this issue, an efficient Exemplar Colorization Network (ECNet)-based chroma intra prediction method is proposed in this paper, in which the colorization relationship between reference luma and chroma components is exploited to predict the chroma components for the current luma component. Inspired by the principle that semantic information in an image exhibits short-range continuity, a Spatial-consistency-based Colorization Transfer Network (SCTNet) is proposed, which builds and transfers colorization representations of neighboring reference blocks for chroma prediction. To improve the chroma prediction capability of SCTNet, a colorization learning module is developed to learn the robust mapping relationship from the luma component to the chroma component in a region-to-pixel manner, and a weight-adaptive reconstruction module is designed to adaptively utilize reference information from neighboring blocks to generate an initial prediction result. In addition, to further improve the accuracy of chroma intra prediction, a multi-reference-based chroma refinement network is proposed, which simultaneously uses the spatial information of neighboring reference chroma blocks and the current luma block to eliminate blocking and color-bleeding artifacts in the initial prediction result. Experimental results demonstrate that our proposed ECNet outperforms the state-of-the-art chroma intra prediction methods in terms of coding performance.

The Versatile Video Coding (VVC) is the latest video coding standard that includes advanced intra coding techniques such as the chroma separate tree and cross-component linear model to improve the prediction accuracy of chroma components. However, these sophisticated techniques inevitably increase the computational complexity of chroma intra coding. In this paper, we propose a fast chroma intra-prediction mode decision algorithm to reduce the computational complexity of the VVC encoder. Firstly, the mode selection correlation between the current chroma Coding Unit (CU) and its nearest-encoded chroma CU is analyzed. Then, based on the variation of the sum of modulus differences, an early mode decision is proposed for the current chroma CU to determine whether the best coding mode should be traversed or directly inherited from its nearest encoded chroma CU. The experimental results show that the proposed algorithm significantly reduces the computational complexity of chroma intra-prediction and achieves an average of 28% chroma intra encoding time saving with only 0.08-1.43% performance degradation in terms of BD-rate.

A three-dimensional reconstruction method which uses cone-beam data was implemented. Issues concerning implementation are investigated and discussed in detail. Computer simulations were used to determine the amount of image degradation occurring in each of the three steps that comprise the reconstruction method. In addition, computer simulations were used to investigate issues concerning sampling. Improvements in the implementation were made. Additionally, suggestions for future efforts to improve the implementation are made.

Intuitively, if only part of an object is to be reconstructed, the information required to perform the reconstruction should be less than that required to perform global reconstruction (global reconstruction is assumed by every published reconstruction formula with closed format). The reduction of input information could lead to the simplification of data collection and the reduction of implementation cost. The author has explored three basic issues of regional cone-beam tomography: (1) a completeness condition for regional reconstruction, (2) an estimation formula, and (3) a convergence condition. The analysis of the first issue yields requirements for artifact-free reconstruction and a convenient criterion for source curve design. The closed format of the estimation formula developed permits efficient analysis. The discussion of the third issue facilitates guidelines for selecting an appropriate interpolation technique for discrete input data.