Template-generated videos (TGVs), created by applying animation templates to static images, have become increasingly prevalent, producing massive user-generated content with highly consistent motion patterns. However, existing video compression schemes are designed to eliminate motion redundancy within individual videos, while overlooking the shared motion patterns widespread across TGVs. To address this limitation, we propose a novel compression scheme that effectively leverages inter-video motion priors to enhance the compression efficiency of TGVs. Specifically, the proposed scheme operates as a two-stage pipeline. In the first stage, high-quality motion priors are identified from a representative TGV based on spatial texture and prediction error. In the second stage, these motion priors are intelligently integrated to expand the motion representation space beyond the local candidate lists in Merge and AMVP modes, thereby enabling the codec to remove inter-video redundancy. Experimental results on the versatile video coding test model (VTM-23.0) demonstrate consistent coding gains across various compression scenarios for TGVs, achieving average BD-rate savings of$1.07 \%, 1.38 {\%}$, and 1.18% under low-delay P (LDP), low-delay B (LDB), and random access (RA) configurations, respectively.

This paper proposes a novel neural B-frame video compression framework with hybrid implicit motion modeling. In our approach, implicit motion modeling replaces the rate-consuming yet less effective flow-based explicit motion modeling to improve overall RD performance. Specifically, an interpolated frame is first generated from the forward and backward reference frames to enrich the temporal priors. A Hybrid Temporal Prior Extractor (HTPE) is then introduced to exploit these priors, where a hybrid feature extractor combining Content-Aware Depthwise Separable Convolution (CADSC) and Linear Attention Duality (LAD) adaptively captures local and global temporal features, respectively. Finally, the enriched temporal prior features are leveraged in the main encoder/decoder to enable implicit motion modeling, and are further integrated into the entropy model to improve the accuracy of entropy estimation for the discrete latent representation.

This paper proposes a highly efficient sample adaptive offset (SAO) mode decision algorithm. By leveraging both the directional correlations between the SAO and intra-prediction decisions, and the SAO decisions' spatial correlations, the SAO mode candidates are effectively pruned during the rate-distortion optimization process, accelerating the SAO encoding process with negligible BD-rate loss.

Recently, neural image compression (NIC) has made remarkable progress. Two key parts of NIC are the encoder-decoder and the entropy model. For the encoder-decoder, a larger effective receptive field (ERF) means a stronger transformation ability. Existing methods usually enlarge the ERF at the expense of complexity, which is intolerable. To address this issue, we propose a multi-scale depthwise separable dilated convolution (MSDSDC) to build the encoder-decoder. Specifically, we first construct a depthwise separable dilated convolution (DSDC) by using the depthwise separable strategy in dilated convolution to reduce its complexity. Subsequently, multi-scale features extracted by three DSDCs with varying dilation rates are fused to expand the ERF of the encoder-decoder, consequently enhancing its transformation capability. Besides, we design a multi-distribution mixture entropy model (MDMEM) to further enhance the flexibility of latent representation probability modeling. The experimental results demonstrate that our proposed method achieves the best balance between rate-distortion performance and complexity.

Visually annoying compression artifacts frequently appear in block-based transform coding at low bit rates, due to coarse and independent quantization of transform coefficients in coding blocks. This paper presents a subband adaptive modeling framework for reducing quantization artifacts. In this framework, each patch is jointly regularized by bandwise distribution priors adaptively learned in its PCA transform domain together with a quantization constraint prior in the DCT domain. Since the compression artifacts influence the covariance statistics of coded image patches remarkably, external images are utilized to provide more robust PCA domains for patch sparse modeling. Instead of using a global distribution model for all patches, the distribution prior of each patch is adaptively learned from similar patches within the compressed image itself to address the non-stationarity of image signals. The coefficients in different PCA bands are regularized unequally according to the learned priors. Experimental results show that the proposed scheme outperforms existing schemes in terms of both the objective and the perceptual qualities.

Summary form only given: Based on observations that the visual quality has strong correlation with image gradients, gradient based image SoftCast (G-Cast) [1] advocates to convey visual information by delivering image gradients. In G-Cast, both horizontal gradients and vertical gradients needs to be transmitted, even if the channel bandwidth is insufficient. This paper propose to send out the random projection measurements of the gradients instead of delivering gradients directly, so that data size can be reduced to an arbitrary ratio and channel bandwidth occupation can be lowered. We name this scheme as compressive gradient based SoftCast (CG-Cast). At CG-Cast sender, after generated by gradient transform, the gradients are down-sampled by random projection, sample rate of which is set according to the channel bandwidth condition. Then the produced measurements are sent out for raw OFDM transmission. A few lowfrequency components are also transmitted to tell the global luminance. At CG-Cast receiver, the received noisy measurements are used for compressive gradient based reconstruction procedure, which utilizes sparsity in gradient domain and non-local similarity in spatial domain [2]. The proposed method is compared with SoftCast [3] and compressive sensing (CS) in bandwidth limited and power constrained scenarios. To make fair comparison, these three schemes are tested under the same channel signal-to-noise ratio (CSNR) conditions to transmit equal amount of data for reconstruction, using equivalent power and bandwidth. CG-Cast outperforms SoftCast and CS in terms of SSIM and gradient signal-to-noise ratio (GSNR) at different bandwidth ratios. Comparing with SoftCast in different channel conditions, the average SSIM gain of all the tested images varies from 0.04 to 0.13, and the average GSNR gain ranges from 1.5dB to 2.9dB. CS is rather unstable in noisy conditions. SoftCast performs better than CS because of its power allocation.

Gaussian random matrix (GRM) has been widely used to generate linear measurements in compressive sensing (CS) of natural images. However, in practice, there actually exist two problems with GRM. One is that GRM is non-sparse and complicated, leading to high computational complexity and high difficulty in hardware implementation. The other is that regardless of the characteristics of signal the measurements generated by GRM are also random, which results in low efficiency of compression coding. In this paper, we design a novel local structural measurement matrix (LSMM) for block-based CS coding of natural images by utilizing the local smooth property of images. The proposed LSMM has two main advantages. First, LSMM is a highly sparse matrix, which can be easily implemented in hardware, and its reconstruction performance is even superior to GRM at low CS sampling sub rate. Second, the adjacent measurement elements generated by LSMM have high correlation, which can be exploited to greatly improve the coding efficiency. Furthermore, this paper presents a new framework with LSMM for block-based CS coding of natural images, including measurement generating, measurement coding and CS reconstruction. Experimental results show that the proposed framework with LSMM for block-based CS coding of natural images greatly enhances the existing CS coding performance when compared with other state-of-the-art image CS coding schemes.

Conventional image and video communication systems are usually designed with the objective being to maximize the fidelity of reconstructed images measured by mean square errors (MSE). It is well known that the fidelity metric MSE may not reflect the visual quality perceived by human eyes. Recent advancements in image quality assessment tell us that the structural similarity (SSIM), especially the gradient similarity, reveals the perceptual fidelity of images more reliably. Inspired by this observation, this paper proposes a new image communication approach, which conveys the visual information in an image by transmitting the image gradients and recovers the image from the received gradient data at decoder side using statistical image prior knowledge. In particular, we designed a gradient-based image SoftCast scheme for wireless scenarios. Experimental results show that the proposed scheme can produce reconstruction images with much better perceptual quality. The advantage in perceptual quality is verified by the quality improvement measured by the metrics SSIM and gradient signal-to-noise ratio (GSNR).

Video broadcasting is a popular application of wireless network. However, the existing layered approaches can hardly accommodate users with diverse channel conditions as analog communication can do. The newly emerged `soft cast' approach, utilizing soft broadcast, provides smooth multicast performance but is not very efficient in inter frame compression. In this work, we propose a motion-aligned wireless video multicast scheme DCAST. Instead of using conventional close loop prediction (CLP), DCAST is based on distributed source coding (DSC) theory. This helps DCAST to avoid error propagation but still achieve high compression efficiency in inter frame coding. DCAST outperforms soft cast 5dB in video PSNR while maintaining the similar graceful degradation feature as soft cast.