We present a novel spherical image compression approach based on Spherical Transformer Block(STB) that takes advantage of HEALPix's hierarchical structure, as illustrated in Figure 1. More specifically, we exploit the hierarchical properties of HEALPix to partition the sphere into several smaller windows and apply multi-head attention within each window, thereby alleviating the computational burden of the spherical Transformer. However, as there is no communication between neighboring windows, we incorporate spherical convolution to aggregate information from adjacent windows. In addition, we propose an inverted residual bottleneck module for feature embedding and a feedforward neural network. Experimental results show that our method outperforms the OSLO [1] approach in terms of performance, while also achieving faster codec times.

End-to-end learned image compression exploits the expressive power of nonlinear transform modules to de-correlate the spatial redundancies of image contents. Due to its long-range attention scheme, transformer-based transforms can explore more global features for better reconstruction. However, transformer modules bring in indispensable computational costs, and the coarse utilization of transformer in learned image compression cannot meet the coding efficiency. In this paper, we propose a novel graph-structured swin-transformer for learned image compression, shown in Figure 1 . We assume that the global receptive field of attention map should be sparse not dense, while the local neighboring correlations must be strong.

Recently, point-based point cloud geometry compression has attracted great attention due to its superior performance at low bit rates. However, lacking an efficient way to represent the local geometric correlation well, most existing methods [1, 2, 3] can hardly extract fine local features accurately. Thus it’s difficult for them to obtain high-quality reconstruction of local geometry of point clouds.

In this paper, we present a deep correlated image set compression scheme based on Distributed Source Coding(DSC) and multi-scale image fusion. As there exists strong correlation among images in a similar image set, we propose to utilize such correlation to generate side information at decoder side for each image in the set. Specifically, a reference structure of the image set is generated by building a minimum spanning tree according to the similarity between two images at encoder. With the reference structure, the side information of each image to be decoded can be generated based on the decoded reference image. And our network learns the correlation between an image and its side information in the training phase. Based on the principle of DSC, the side information can provide additional information such as rich details at decoder side. To make full use of the side information, the initially decoded image and the additional side information are fused at different scales. A decompressed image enhancement network is introduced to reduce the compression artifacts of the decoded images. Extensive experimental results compared with other mainstream methods validate the superior performance of our scheme in both terms of subjective and objective quality.

Removing undesirable reflections from a single image captured through a glass surface is of broad application to various image processing and computer vision tasks, but it is an ill-posed and challenging problem. Existing traditional single image reflection removal(SIRR) methods are often less efficient to remove reflection due to the limited description ability of handcrafted priors. State-of-the-art learning based methods often cause instability problems because they are designed as unexplainable black boxes. In this paper, we present an explainable approach for SIRR named model-guided unfolding network(MoG-SIRR), which is unfolded from our proposed reflection removal model with non-local autoregressive prior and dereflection prior. In order to complement the transmission layer and the reflection layer in a single image, we construct a deep learning framework with two streams by integrating reflection removal and non-local regularization into trainable modules. Extensive experiments on public benchmark datasets demonstrate that our method achieves superior performance for single image reflection removal.

The Spherical Measure Based Spherical Image Representation (SMSIR) has nearly uniformly distributed pixels in the spherical domain with effective index schemes. Based on SMSIR, the spherical wavelet transform can be efficiently designed, which can capture the spherical geometry feature in a compact manner and provides a powerful tool for spherical image compression. In this paper, we propose an efficient compression scheme for SMSIR images named Spherical Set Partitioning in Hierarchical Trees (S-SPIHT) using the spherical wavelet transform, which exploits the inherent similarities across the subbands in the spherical wavelet decomposition of a SMSIR image. The proposed S-SPIHT can progressively transform spherical wavelet coefficients into bit-stream, and generate an embedded compressed bit-stream that can be efficiently decoded at several spherical image quality levels. The most crucial part of our proposed S-SPIHT is the redesign of scanning the wavelet coefficients corresponding to different index schemes. We design three scanning methods, namely ordered root tree index scanning (ORTIS), dyadic index progressive scanning(DIPS) and dyadic index cross scanning(DICS)to efficiently reorganize the wavelet coefficients. These methods can effectively exploit the self-similarity between sub-bands and the fact that the high-frequency sub-bands mostly contain insignificant coefficients. Experimental results on widely-used datasets demonstrate that our proposed S-SPIHT outperforms the straightforward SPIHT for SMSIR images in terms of PSNR, S-PSNR and SSIM.

Summary form only given. We present an architecture for digital HDTV video decoding (MPEG-2 MP@HL), based on dual decoding data paths controlled in a block layer synchronization manner and an efficient write back scheme. Our fixed schedule controller synchronizes the baseline units on a block basis in both data-paths. This scheme reduces embedded buffer sizes within the decoder and eliminates a lot of external memory bus contentions. In our write back scheme, the display DRAM is physically separated from the anchor picture DRAM, and is added to the display engine, not to the bus. The slight increase in overall DRAM size is acceptable due to the low DRAM cost today. This improves the parallelism in accessing anchor and display pictures and saves about 80 clock cycles per macroblock (based on a 81 MHz clock). Compared to the other decoding approaches such as the slice bar decoding method and the crossing-divided method, this scheme reduces memory access contentions and the amount of embedded local memory required. Our simulations show that with a relatively low speed 81 MHz clock, our architecture uses fewer than the 332 cycles (required real-time decoding upper bound), to decode each macroblock, without a high cost in overall chip area.

Summary form only given. This paper describes the design of a high speed motion estimator using the 2-D log search algorithm. The architecture consists of 5 simple processing elements (PE) where each PE is capable of computing the sum-of-absolute-difference (SAD) to exploit the parallelism. For each step in the 2-D log search procedure, the 5 SADs of the 5 search points are computed in parallel. The design is implemented using Verilog and synthesized using Synopsys. Simulations show that the architecture is able to generate the motion vector for each 16/spl times/16 macroblock in 14.58 /spl mu/s for 3-step log search, and 24.30 /spl mu/s for 5-step log search. The architecture is thus well suited for encoding MPEG2 video up to MP@ML.

In VQ design, large codebooks are required for better image quality, while less codebook search and memory are desired for better computational efficiency and compression ratio. In this paper, we present an approach to increase the amount of VQ codebook patterns to improve image quality without a large overhead on the codebook memory and search time. Basically, our approach characterizes a pattern into a key value, which is used in codebook search.