BACKGROUNDS: It has been observed that high levels of enhancer of zeste homolog 2 (EZH2) expression are associated with unsatisfactory prognoses and can be found in a wide range of malignancies. However, the effects of EZH2 on Lung Adenocarcinoma (LUAD) remain elusive. Through the integration of bioinformatic analyses, the present paper sought to ascertain the effects of EZH2 in LUAD. METHODS: The TIMER and UALCAN databases were applied to analyze mRNA and protein expression data for EZH2 in LUAD. The result of immunohistochemistry was obtained from the HPA database, and the survival curve was drawn according to the library provided by the HPA database. The LinkedOmics database was utilized to investigate the co-expressed genes and signal transduction pathways with EZH2. Up- and down-regulated genes from The Linked Omics database were introduced to the CMap database to predict potential drug targets for LUAD using the CMap database. The association between EZH2 and cancer-infiltrating immunocytes was studied through TIMER and TISIDB. In addition, this paper explores the relationship between EZH2 mRNA expression and NSCLC OS using the Kaplan-Meier plotter database to further validate and complement the research. Furthermore, the correlation between EZH2 expression and EGFR genes, KRAS genes, BRAF genes, and smoking from the Cancer Genome Atlas (TCGA) database is analyzed. RESULTS: In contrast to paracancer specimens, the mRNA and protein levels of EZH2 were higher in LUAD tissues. Significantly, high levels of EZH2 were associated with unsatisfactory prognoses in LUAD patients. Additionally, the coexpressed genes of EZH2 were predominantly associated with numerous cell growth-associated pathways, including the cell cycle, DNA replication, RNA transport, and the p53 signaling pathway, according to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. The results of TCGA database revealed that the expression of EZH2 was lower in normal tissues than in lung cancer tissues (p < 0.05). Smoking was associated with elevated EZH2 expression (p < 0.001). EZH2 was highly expressed in lung cancers with positive KRAS expression, and the correlation was significant in lung adenocarcinoma (r = 0.3129, p < 0.001). CMap was applied to determine the top 15 positively correlated drugs/molecules and the top 15 negatively correlated drugs/molecules. MK-1775, MK-5108, fenbendazole, albendazole, BAY-K8644, evodiamine, purvalanol-a, mycophenolic-acid, PHA-793887, and cyclopamine are potential drugs for patients with lung adenocarcinoma and high EZH2 expression. CONCLUSIONS: Highly expressed EZH2 is a predictor of a suboptimal prognosis in LUAD and may serve as a prognostic marker and target gene for LUAD. The underlying cause may be associated with the synergistic effect of KRAS, immune cell infiltration, and metabolic processes.

The recent advances of hardware technology have made the intelligent analysis equipped at the front-end with deep learning more prevailing and practical. To better enable the intelligent sensing at the front-end, instead of compressing and transmitting visual signals or the ultimately utilized top-layer deep learning features, we propose to compactly represent and convey the intermediate-layer deep learning features with high generalization capability, to facilitate the collaborating approach between front and cloud ends. This strategy enables a good balance among the computational load, transmission load and the generalization ability for cloud servers when deploying the deep neural networks for large scale cloud based visual analysis. Moreover, the presented strategy also makes the standardization of deep feature coding more feasible and promising, as a series of tasks can simultaneously benefit from the transmitted intermediate layer features. We also present the results for evaluations of both lossless and lossy deep feature compression, which provide meaningful investigations and baselines for future research and standardization activities.

After the works on the state-of-the-art High Efficiency Video Coding (HEVC) standard, the standard organizations continued to study the potential video coding technologies for the next generation of video coding standard, named Versatile Video Coding (VVC). Transform is a key technique for compression efficiency, and core experiment 6 (CE6) is carried out to explore the transform related coding tools. In this paper, we propose a novel separable transform based on Karhunen-Loève Transform (KLT) to eliminate the horizontal and vertical correlations in the residual samples of intra coding. In the proposed method, the weaknesses of the traditional KLT are addressed. The separable KLT is developed as an alternative transform type in addition to DCT-II, and the transform matrices from 4×4 to 64×64 are trained from intra residual samples. Experimental results show the proposed method can achieve 2.7% bitrate saving averagely on top of the reference software of VVC (VTM-1.1), and the consistent performance improvement on test set also validates the strong generalization capacity of the proposed separable KLT.

With the unprecedented success of deep learning in computer vision tasks, many cloud-based visual analysis applications are powered by deep learning models. However, the deep learning models are also characterized with high computational complexity and are task-specific, which may hinder the large-scale implementation of the conventional data communication paradigms. To enable a better balance among bandwidth usage, computational load and the generalization capability for cloud-end servers, we propose to compress and transmit intermediate deep learning features instead of visual signals and ultimately utilized features. The proposed strategy also provides a promising way for the standardization of deep feature coding. As the first attempt to this problem, we present a lossy compression framework and evaluation metrics for intermediate deep feature compression. Comprehensive experimental results show the effectiveness of our proposed methods and the feasibility of the proposed data transmission strategy. It is worth mentioning that the proposed compression framework and evaluation metrics have been adopted into the ongoing AVS (Audio Video Coding Standard Workgroup) - Visual Feature Coding Standard.

The state-of-the-art high efficiency video coding (HEVC) standard provides a significant improvement relative to H.264/AVC, with almost 50% bitrate reduction. However, there are still requirements to further improve the coding performance of HEVC. In this paper, we reveal some problems in both intra and inter prediction by giving detailed observations, and solve them by further exploring spatial and temporal correlations. First, it is noteworthy that large boundary distortions in intra prediction blocks arise from the simplistic extrapolation operation with partial reference samples. To reduce these boundary distortions, we propose an enhanced prediction boundary filtering method based on generalized Gaussian distribution functions, and extend this method to merge prediction. Second, we combine intra and inter prediction to create a novel prediction method, in which spatial and temporal correlations are simultaneously considered. In addition, the existing merge mode can only provide accurate prediction results for simple movements, owing to the lack of motion estimation. To improve the flexibility, we optimize the existing merge mode by intensifying the temporal correlation with a simple motion search process. The experimental results show that the proposed methods achieve consistent bitrate savings, 1.3% for all intra, 1.5% for random access, 2.5% (up to 4.2%) for low delay P, and 1.2% for low delay B configuration. The encoding time only rises by 70% - 80%, and the decoding time increases negligibly.

Merge prediction is a practical inter-technique in HEVC, which can significantly improve the coding efficiency, especially for homogeneous regions in video sequences. In this paper, a motion aided merge mode (MAMM) is proposed to achieve a better trade-off between the prediction accuracy and bit rate. Different from the traditional merge mode in HEVC, MAMM is accomplished by a small motion obtained by searching in a specific search region. The search range is comprised of a number of points with high occurrence possibilities. The motion vector difference (MVD) is coded by Huffman coding in MAMM and the Huffman coding table is generated according to the statistical frequency of each possible MVD value. The proposed method is implemented on top of the HEVC reference software (HM −16.15), and experimental results show that 0.6% BD-rate reduction is achieved under Random Access (RA) configuration.

MPEG has produced standards that have provided the industry with the best video compression technologies. To address diverse Internet needs, MPEG issued a Call for Proposals (CfP) for Internet video coding (IVC) in July, 2011. The anticipation is that any patent declaration associated with the baseline profile of this standard will indicate that the patent owner is prepared to grant a free of charge license to an unrestricted number of applicants worldwide. Three codecs have responded to the CfP: Web video coding (WVC), video coding for browsers (VCB), and IVC. WVC is in fact the AVC baseline, and VCB uses the same coding tools as VP8. IVC has been developed in MPEG from scratch by combining well-known existing technology elements and new coding tools with royalty-free declarations. In June 2015, the IVC project was approved as ISO/IEC 14496-33 (MPEG-4 IVC). This standard can be highly beneficial for video services in the Internet domain. This paper describes the main coding tools used in IVC, and evaluates its objective and subjective performances compared with WVC, VCB, and AVC high profile (AVC HP). The experimental results show that IVC's compression performance is approximately equal to that of the AVC HP for typical operational settings, both for streaming and low-delay applications, and is superior to WVC and VCB.

This paper presents a novel intra prediction method for inter pictures (i.e. P pictures and B pictures), denominated enhanced intra prediction (EIP). The traditional intra prediction only uses the reconstructed pixels to the left and above to derive intra prediction blocks. While the proposed method combines the left-above and right-below pixels to strengthen the prediction efficiency of the intra blocks in inter pictures. For accessing the pixels below and to the right of intra coding units (CUs), the encoding and decoding structures are adjusted to guarantee that all the inter CUs are reconstructed before all the intra CUs. With more available reference samples around the intra CUs, EIP achieves better prediction results. The proposed method is implemented on top of the H.265/HEVC reference software (HM-16.12), and the experimental results show that approximate 0.5% BD-rate reduction is achieved under Random Access (RA) and Low Delay P (LP) configurations.

This paper presents a group of novel intra boundary filters to refine the intra prediction blocks. The existing intra boundary filters only adjust prediction blocks for a few special intra modes. While the proposed method extends the boundary filtering to all intra modes, and allows to modify up to 6 rows and columns at the boundaries of intra prediction blocks. A group of filters are designed by comprehensively considering the spatial similarity between reference samples and predicted pixels, as well as the statistical characters of boundary prediction distortion. In addition, low computational complexity is also essential to make the proposed filters simple and effective. By involving the proposed boundary filters to revise the intra prediction blocks, better prediction results can be achieved. The proposed method is implemented on top of the H.265/HEVC reference software (HM-16.12), and the experimental results show that 1.0% (up to 1.5%) BD-rate reduction is achieved under All Intra (AI) configuration.

3D sensing and content capturing have made significant progress in recent years and the MPEG standardization organization is launching a new project on immersive media with point cloud compression (PCC) as one key corner stone. In this work, we introduce a new binary tree based point cloud partition and explore the graph signal processing tools, especially the graph transform with optimized Laplacian sparsity, to achieve better energy compaction and compression efficiency. The resulting rate-distortion operating points are convex-hull optimized over the existing Lagrangian solutions. Simulation results on the latest high quality point cloud content from the MPEG PCC demonstrate the transform efficiency and rate-distortion (R-D) optimal potential of the proposed solutions.

Panoramic video provides an immersive experience by presenting a 360° spherical video content. Due to the limitations of coding and storage technology, the spherical panoramic video needs to be projected onto the two-dimensional plane for storage and encoding. In this paper, we propose a polar square projection scheme. We project the area near the poles of the sphere into two square planes and a latitude circle on sphere is projected to a square circle on squares plane, in addition, the rest of area on sphere is projected into a rectangle by means of equal area projection. Experimental results show our proposed projection can obtain a gain of 11.63% BD-rate compared to the equirectangular projection.

The second generation of the audio video coding standard (AVS2), which has been issued as IEEE 1857.4, doubles the coding efficiency of AVS1 and H.264/AVC. However, the advanced techniques in AVS2 also dramatically increase the computational complexity. The research on a commercial encoder for AVS2 is still at an early stage. In this paper, we propose the first fast intra-encoding platform for AVS2, which we term iAVS2. The platform uses numerous speedup methods, including code optimization, single-instruction multiple-data acceleration, and fast algorithms for almost all the time-consuming modules. To meet different application scenarios, five different speed levels are designed in iAVS2 to provide flexible tradeoffs between compression efficiency and encoding speed. The experimental results show that the fastest speed level of iAVS2 can accelerate the AVS2 reference software (RD12.0) by 144× on average. Compared with the well-known HEVC encoder platform ×265, iAVS2 can provide over 10% bit rate saving at a similar coding speed. For the UHD and 1080 sequences, iAVS2 outperforms x265 at all speed levels.

In this paper, a novel self-adaptive curve, based on human visual model (HVM), is proposed for recovering details from low dynamic range (LDR) digital photographs, which are under-exposed or over-exposed or both. In order to improve the perceptual visibility, we utilize HVM to construct our method, which is able to take advantage of entire dynamic range to enhance the contrast of images. Extensive experiments demonstrate that our method consistently achieves satisfying results for unwell-exposed LDR photographs.