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Image compression has been investigated as a fundamental research topic for many decades. Recently, deep learning has achieved great success in many computer vision tasks, and is gradually being used in image compression. In this paper, we present a lossy image compression architecture, which utilizes the advantages of convolutional autoencoder (CAE) to achieve a high coding efficiency. First, we design a novel CAE architecture to replace the conventional transforms and train this CAE using a rate-distortion loss function. Second, to generate a more energycompact representation, we utilize the principal components analysis (PCA) to rotate the feature maps produced by the CAE, and then apply the quantization and entropy coder to generate the codes. Experimental results demonstrate that our method outperforms traditional image coding algorithms, by achieving a 13.7% BD-rate decrement on the Kodak database images compared to JPEG2000. Besides, our method maintains a moderate complexity similar to JPEG2000.

Compression has been an important research topic for many decades, to produce a significant impact on data transmission and storage. Recent advances have shown a great potential of learning image and video compression. Inspired from related works, in this paper, we present an image compression architecture using a convolutional autoencoder, and then generalize image compression to video compression, by adding an interpolation loop into both encoder and decoder sides. Our basic idea is to realize spatial-temporal energy compaction in learning image and video compression. Thereby, we propose to add a spatial energy compaction-based penalty into loss function, to achieve higher image compression performance. Furthermore, based on temporal energy distribution, we propose to select the number of frames in one interpolation loop, adapting to the motion characteristics of video contents. Experimental results demonstrate that our proposed image compression outperforms the latest image compression standard with MS-SSIM quality metric, and provides higher performance compared with state-of-the-art learning compression methods at high bit rates, which benefits from our spatial energy compaction approach. Meanwhile, our proposed video compression approach with temporal energy compaction can significantly outperform MPEG-4 and is competitive with commonly used H.264. Both our image and video compression can produce more visually pleasant results than traditional standards.

Different from the traditional quaternary tree (QT) structure utilized in the previous generation video coding standard H.265/HEVC, a brand new partition structure named quadtree with nested multi-type tree (QTMT) is applied in the latest codec H.266/VVC. The introduction of QTMT brings in superior encoding performance at the cost of great time-consuming. Therefore, a fast intra partition algorithm based on variance and Sobel operator is proposed in this paper. The proposed method settles the novel asymmetrical partition issue in VVC by well balancing the reduction of computational complexity and the loss of encoding quality. To be more concrete, we first terminate further splitting of a coding unit (CU) when the texture of it is judged as smooth. Then, we use Sobel operator to extract gradient features to decide whether to split this CU by QT, thus terminating further MT partitions. Finally, a completely novel method to choose only one partition from five QTMT partitions is applied. Obviously, homogeneous area tends to use a larger CU as a whole to do prediction while CUs with complicated texture are prone to be divided into small sub-CUs and these sub-CUs usually have different textures from each other. We calculate the variance of variance of each sub-CU to decide which partition will distinguish the sub-textures best. Our method is embedded into the latest VVC official reference software VTM-7.0. Comparing to anchor VTM-7.0, our method saves the encoding time by 49.27% on average at the cost of only 1.63% BDBR increase. As a traditional scheme based on variance and gradient to decrease the computational complexity in VVC intra coding, our method outperforms other relative existing state-of-the-art methods, including traditional machine learning and convolution neural network methods. INDEX TERMS Asymmetric block size, fast partition decision, intra prediction, quadtree with multi-type tree, versatile video coding YIBO FAN received the B.E. degree in electronics and engineering from

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