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Adaptive Chroma Subsampling-Binding and Luma-Guided Chroma Reconstruction Method for Screen Content Images

机译:屏幕内容图像的自适应色度二次采样绑定和亮度引导色度重构方法

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In this paper, we propose a novel adaptive chroma subsampling-binding and luma-guided (ASBLG) chroma reconstruction method for screen content images (SCIs). After receiving the decoded luma and subsampled chroma image from the decoder, a fast winner-first voting strategy is proposed to identify the used chroma subsampling scheme prior to compression. Then, the decoded luma image is subsampled as the identified subsampling scheme was performed on the chroma image such that we are able to conclude an accurate correlation between the subsampled decoded luma image and the decoded subsampled chroma image. Accordingly, an adaptive sliding window-based and luma-guided chroma reconstruction method is proposed. The related computational complexity analysis is also provided. We take two quality metrics, the color peak signal-to-noise ratio (CPSNR) of the reconstructed chroma images and SCIs and the gradient-based structure similarity index (CGSS) of the reconstructed SCIs to evaluate the quality performance. Let the proposed chroma reconstruction method be denoted as ‘ASBLG’. Based on 26 typical test SCIs and 6 JCT-VC test screen content video sequences (SCVs), several experiments show that on average, the CPSNR gains of all the reconstructed UV images by 4:2:0(A)-ASBLG, SCIs by 4:2:0(MPEG-B)-ASBLG, and SCVs by 4:2:0(A)-ASBLG are 2.1, 1.87, and 1.87 dB, respectively, when compared with that of the other combinations. Specifically, in terms of CPSNR and CGSS, CS-ASBLG for the test SCIs and CS-ASBLG for the test SCVs outperform the existing state-of-the-art comparative combinations, where CS and CS denote the luma-aware based chroma subsampling schemes by Wang et al.
机译:在本文中,我们提出了一种用于屏幕内容图像(SCI)的新型自适应色度二次采样绑定和亮度引导(ASBLG)色度重建方法。在从解码器接收到解码后的亮度和子采样色度图像后,提出了一种快速的获胜者优先投票策略,以在压缩之前识别使用的色度子采样方案。然后,由于对色度图像执行了已识别的子采样方案,因此对解码的亮度图像进行了子采样,因此我们能够得出子采样的解码亮度图像和解码的子采样色度图像之间的准确相关性。因此,提出了一种基于自适应滑动窗口的亮度引导色度重建方法。还提供了相关的计算复杂度分析。我们采用两个质量指标,即重建色度图像和SCI的色峰信噪比(CPSNR)和重建SCI的基于梯度的结构相似性指数(CGSS)来评估质量性能。让建议的色度重建方法表示为“ ASBLG”。根据26个典型的测试SCI和6个JCT-VC测试屏幕内容视频序列(SCV),几个实验表明,平均而言,所有重构的UV图像的CPSNR增益为4:2:0(A)-ASBLG,SCI为与其他组合相比,4:2:0(MPEG-B)-ASBLG和4:2:0(A)-ASBLG的SCV分别为2.1、1.87和1.87 dB。具体而言,就CPSNR和CGSS而言,用于测试SCI的CS-ASBLG和用于测试SCV的CS-ASBLG优于现有的最新比较组合,其中CS和CS表示基于亮度感知的色度子采样方案由Wang等。

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