Real-time video communication is critically hampered by packet loss. Building on the neural video codec (NVC) and exploiting the statistics of its latent representations, we propose a channel importance-aware redundancy framework that eliminates insignificant channels and reallocates high redundancy to protect important channels for loss-resilient video coding. We first develop a channel-wise importance evaluation method based on the in-distribution (IND) region length criterion [1] for latent representations of prediction residuals and motion vectors produced by NVCs. Consequently, we derive a heuristic that adapts the number of protected channels to the packet loss rate (PLR). More channels are retained at low PLRs to improve video quality, while only a few critical channels are kept and assigned higher redundancy for enhanced robustness at high PLRs. Experimental results show that the proposed method provides more graceful quality degradation and maintains significant performance advantages, particularly under high PLRs, as illustrated in Figure 1.

Despite strong generalization ability, large language models (LLMs) are resource-intensive to deploy. Post-training quantization (PTQ) effectively compresses pretrained LLMs without retraining. Although recent studies target extremely low-bit quantization, most methods rely on extra parameters and the effective bit-width usually exceeds 2 bits. We introduce a saliency-guided mixed-precision PTQ framework that achieves an average bit-width of 1.70 bits while maintaining competitive performance. The proposed method allocates bit-widths across weight groups under a strict total budget, guided by a cross-precision saliency metric. The key innovation is a learnable affine transformation consisting of rotation, scaling, and shifting to align quantization grids with local weight distributions to minimize error. Experiments show that the proposed method matches state-of-the-art low-bit PTQ performance, while reducing the average bit-width by approximately 0.55 bits in Table 1.

Learned image compression (LIC) has recently emerged as a promising alternative to realize transform-based coding. Contrary to widely studied transform and entropy modeling in LIC, most LIC methods are limited to uniform scalar quantization and existing vector quantization (VQ) methods are restricted by the soft-to-hard quantization strategy. In this paper, we propose a novel entropy relaxed lattice vector quantization (ERLVQ) scheme for LIC. ERLVQ achieves a tractable dimension-wise approximation to the probability mass function for the complex Voronoi regions in a general class of lattices. Instead of directly performing probabilistic estimation on the lattices, ERLVQ equivalently aligns complex Voronoi regions with a set of complete and non-overlapping hypercubes in a projected parametric domain. Space covering technique is then developed to generating hypercubes with their centers fixed to integer projected lattice points and their volumes equaling the Voronoi regions to enable separable dimension-wise estimation of multi-variate probability mass functions. The proposed ERLVQ is the first to adapt to arbitrary lattices for VQ based on LIC and can be embedded into existing LIC methods. Experimental results demonstrate that the proposed ERLVQ is superior to the uniform scalar quantization (USQ) and existing lattice VQ methods [1] on prevailing LIC baselines, as shown in Table 1 .

Network quantization is promising to alleviate the storage and deployment burden for deep neural networks. However, existing methods focus on directly quantizing the network parameters but ignore the correlations between the initialized parameters (generated by random seeds) and the optimized parameters (obtained when the network training converges). In this paper, we reveal the redundancy between the initialized and optimized parameters and propose a novel quantization scheme for network parameters with rescaled initialization. We develop a novel quantization scheme for quantizing the delta parameters, i.e. , the difference between the optimized parameters and the rescaled initialization, as shown in Figure 1 . The scale is learned to adapt the weight regularization in training by minimizing the rounding error and penalizing the long-tailed distribution of the delta parameter. Adaptive rounding is further presented to calibrate the rounding direction of the delta parameter via a learnable bias. The proposed method can achieve high-efficiency prediction of network parameters and consistent performance gains with a trivial overhead of single random seed. Extensive experimental results demonstrate its superior performance on various popular network architectures, as exemplified in Table 1 .

End-to-end learned image compression (LIC) has become promising alternatives for lossy image compression. However, deployments of LIC models are limited in real-world applications due to excessive network parameters and high computational complexity. Existing LIC models realized with integer networks are significantly de-graded in rate-distortion (R-D) performance. In this paper, we propose a novel fully integerized LIC model that simultaneously achieves channel-wise weight and 8-bit activation quantization for alleviating the loss of R-D performance. For weight quantization, we develop an internal bit width increment (IBWI) via nonlinear logarithmic mapping to convert the stored low-precision integer weights to high bit-width weights for inference. Moreover, outlier channel splitting (OCS) [1] is employed to address large outliers of weight distribution by duplicating channels and constrain the integer weights within the scope of INT8. For activation quantization, we leverage activation equalization to balance the channel-wise distribution of activations. Experimental results demonstrate that the proposed method achieves a reduction of 75% storage cost with subtle performance loss compared to full-precision pre-trained models, and outperforms existing integer-only networks, as shown in Table 1.

Quantizing one single deep neural network into multiple compression rates (precisions) has been recently considered for flexible deployments in real-world scenarios. In this paper, we propose a novel scheme that achieves progressive bit-width allocation and joint training to simultaneously optimize mixed-precision quantized networks under multiple compression rates. Specifically, we develop a progressive bit-width allocation with switchable quantization step size to enable mixed-precision quantization based on analytic sensitivity of network layers under multiple compression rates. Furthermore, we achieve joint training for quantized networks under different compression rates via knowledge distillation to exploit their correlations based on the shared network structure. Experimental results show that the proposed scheme outperforms AdaBits [1] in various networks on CIFAR-10 and ImageNet.

Existing entropy models in learned image compression are cumbersome to generate fixed mean and variance for estimating Gaussian distributions for latent representation. In this paper, we propose a novel entropy model based on Gaussian process regression (GPR) that flexibly predicts the mean of Gaussians with posterior distributions characterized by covari-ance functions spanned in the high-dimensional feature space. Furthermore, we develop the rate-distortion optimization based on the proposed entropy model by approximating the bitrates with the evidence lower bound (ELBO) derived via variational inference for GPR. The proposed model can be seamlessly integrated into existing end-to-end optimized frame-works by substituting the masked convolution based autoregressive models. Experimental results demonstrate that the proposed model outperforms conventional image compression methods such as JPEG2000 and BPG, as well as recent learning based methods on the Kodak dataset in terms of rate-distortion performance.

Existing methods for cross-component prediction focus on the chroma intra pre-diction but neglect the chroma prediction across multiple frames. In this paper, we propose a novel compression framework that leverages chroma frame sampling and inter-frame chroma prediction to address this problem. Specifically, chroma frame sampling discards the chroma components in inter-predicted frames to further reduce the bit consumption, whereas inter-frame chroma prediction recovers the discarded chroma components with optical flow estimation and post-training optimization for a guaranteed fidelity. To our best knowledge, this paper is the first attempt to achieve deep learning-based inter prediction of chroma components. To accommodate the proposed framework, we redesign the HEVC codec to enable hybrid coding of luma and chroma components. Experimental results show that the proposed framework achieves up to 0.76% BD-rate reduction when compared to standard HEVC.

3-D point clouds rendering solid representations of scenes or objects often carry a tremendous amount of points, compulsorily requesting high-efficiency compression for storage and transmission. In this paper, we propose a novel$p$-Laplacian embedding graph dictionary learning algorithm for 3-D point cloud attribute compression. The proposed method integrates the underlying graph topology to the learned graph dictionary capitalizing on$p$-Laplacian eigenfunctions and leads to parsimonious representations of 3-D point clouds. We further devise alternating optimization with the help of ADMM to efficiently solve the resulting non-convex minimization problem.

We propose a structured pruning method to achieve a light-weighted decoder of learned image compression to accommodate various terminals. The structured pruning method identifies the effectiveness of each channel of decoder via gradient ascent and gradient descent while maintaining the encoder and entropy model. To our best knowledge, this paper is the first attempt to design a structured pruning method for universal pretrained learned image compression. Experimental results demonstrate that the proposed method can reduce about 40% parameters and save 25% inference time at the cost of 0.23 dB BD-PSNR and 4.33% BD-rate change.

Cloud service has been emerging as a promising alternative to handle massive volumes of video sequences triggered by increasing demands on video service, especially surveillance and entertainment. Lossless compression of encoded video bitstreams can further eliminate the redundancies without altering the contents and facilitate the efficiency of cloud storage. In this paper, we propose a novel lossless compression scheme to further compress the video bitstreams generated by the state-of-the-art hybrid coding frameworks like H.264/AVC and HEVC. Different from transcoding, the proposed scheme develops frequency prediction and Macro Block (MB) merging to eliminate the redundancies remained in the intra-and inter-predicted frames with a strict guarantee of video fidelity. To our best knowledge, this paper is the first attempt to realize lossless compression of video bitstreams generated by advanced coding standards H.264/AVC and HEVC. Experimental results demonstrate that the proposed scheme can achieve compression gains of 17.38% and 10.85% on standard test sequences and surveillance videos, respectively.

Deep learning has been widely adopted in compression sensing (CS) to achieve superior reconstruction quality, but is restricted by the black-box architecture in network design and lack of interpretability. In this paper, we propose a novel deep network-based CS framework via unfolding the$\ell_{0}$-constrained convolutional sparse coding (CSC). The proposed method incorporates deep neural networks (DNNs) into the state-of-the-art optimization paradigm of alternating direction method of multipliers (ADMM). CS reconstruction can be improved with the compact and shift-invariant sparse representation under the merits of CSC and DNNs with learnable parameters. Convergence analysis demonstrates that the proposed method is guaranteed to converge, when the network parameters are reused for all the building blocks. Experimental results demonstrate that the proposed method is competitive or outperform the state-of-the-art DNN-based methods in CS of natural images and magnetic resonance imaging (MRI).