Perceptual image compression has recently gained increasing attention, as it aims to reconstruct visually realistic images using generative models. Most existing methods adopt patch-based generative adversarial networks (PatchGAN) for one-step image generation, where adversarial training helps the decoder learn the distribution of natural images. However, this strategy is often coarse-grained, as it focuses mainly on patch-level consistency and overlooks global structural and semantic details. To address this limitation, we propose a simple yet effective Semantic and Spectral Consistency Learning (SSCL) strategy, which complements existing patch-based approaches for more accurate distribution alignment. For semantic consistency, we leverage semantic vision models to extract semantic features. The semantic discriminator, aware of the specific semantics of each image, provides more adaptive and precise feedback. This guides the encoder to retain meaningful information and helps the decoder synthesize detailed textures, without requiring explicit semantic transmission or additional modules. For spectral consistency, we introduce a frequency discriminator that focuses on high-frequency components, helping to reduce artifacts based on spectral priors. Experiments show that SSCL outperforms existing perceptual codecs in terms of visual quality. Compared to MS-ILLM, SSCL achieves 45% to 60% bit-rate savings on CLIC2020 and Kodak datasets, measured by FID and DISTS.

Practically, it is more feasible to collect compact visual features rather than the video streams from hundreds of thousands of cameras into the cloud for big data analysis and retrieval. Then the problem becomes which kinds of features should be extracted, compressed and transmitted so as to meet the requirements of various visual tasks. Recently, many studies have indicated that the activations from the convolutional layers in convolutional neural networks (CNNs) can be treated as local deep features describing particular details inside an image region, which are then aggregated (e.g., using Fisher Vectors) as a powerful global descriptor. Combination of local and global features can satisfy those various needs effectively. It has also been validated that, if only local deep features are coded and transmitted to the cloud while the global features are recovered using the decoded local features, the aggregated global features should be lossy and consequently would degrade the overall performance. Therefore, this paper proposes a joint coding framework for local and global deep features (DFJC) extracted from videos. In this framework, we introduce a coding scheme for real-valued local and global deep features with intra-frame lossy coding and inter-frame reference coding. The theoretical analysis is performed to understand how the number of inliers varies with the number of local features. Moreover, the inter-feature correlations are exploited in our framework. That is, local feature coding can be accelerated by making use of the frame types determined with global features, while the lossy global features aggregated with the decoded local features can be used as a reference for global feature coding. Extensive experimental results under three metrics show that our DFJC framework can significantly reduce the bitrate of local and global deep features from videos while maintaining the retrieval performance.

Encoding multiple videos in parallel and transmitting them as one joint stream over a limited bandwidth have become a popular strategy for broadcasting, which brings an opportunity to allocate different bitrate for each sequence to meet different demands. In this paper, considering visual experience for human beings, we propose a joint rate allocation scheme aims to reach an equal visual quality among all sequences by minimizing the distortion variance of all the sequences (denoted as minVAR problems). Existing methods assigned bits directly in proportion to their complexity measures and we named them as complexity based allocation scheme (CAS) methods. CAS methods rely on the accuracy of the complexity measures which can hardly be improved under limited computing resources. Also complexities may not be directly related to the distortions. To address these problems, we present a novel joint rate-distortion (R-D) based allocation scheme (RDAS) in this paper. Our proposed scheme can fit for different R-D models and in our method we model the R-D relationship with a hyperbolic function (RDAS-H). We also derive a closed-form solution of RDAS-H by a proposed joint R-D relationship. We integrated the RDAS-H method in high efficiency video coding reference software HM16.0. Experimental results demonstrate that our RDAS-H saves 75.29% variance on average over the related CAS-based method, where we apply both low delay and random access configurations with four different overall bandwidths for all classes recommended by the Joint Collaborative Team on Video Coding. Besides, RDAS-H also saves 36.62% variance on average over our previous method. The proposed RDAS-H method improves the performance significantly while requiring negligible computational cost.

With the explosion in the use of cameras in mobile phones or video surveillance systems, it is impossible to transmit a large amount of videos captured from a wide area into a cloud for big data analysis and retrieval. Instead, a feasible solution is to extract and compress features from videos and then transmit the compact features to the cloud. Meanwhile, many recent studies also indicate that the features extracted from the deep convolutional neural networks will lead to high performance for various analysis and recognition tasks. However, how to compress video deep features meanwhile maintaining the analysis or retrieval performance still remains open. To address this problem, we propose a high-efficiency deep feature coding (DFC) framework in this paper. In the DFC framework, we define three types of features in a group-of-features (GOFs) according to their coding modes (i.e., I-feature, P-feature, and S-feature). We then design two prediction structures for these features in a GOF, including a sequential prediction structure and an adaptive prediction structure. Similar to video coding, it is important for P-feature residual coding optimization to make a tradeoff between feature bitrate and analysis/retrieval performance when encoding residuals. To do so, we propose a rate-performance-loss optimization model. To evaluate various feature coding methods for large-scale video retrieval, we construct a video feature coding data set, called VFC-1M, which consists of uncompressed videos from different scenarios captured from real-world surveillance cameras, with totally 1M visual objects. Extensive experiments show that the proposed DFC can significantly reduce the bitrate of deep features in the videos while maintaining the retrieval accuracy.

In this paper we propose an efficient hybrid zero block early detection method for high efficiency video coding (HEVC). Our method detects both genuine zero blocks (GZBs) and pseudo zero blocks (PZBs). For GZB detection, we use a two sum of absolute difference bounds and a one sum of absolute transformed difference threshold to decrease the GZB detection complexity. A fast rate-distortion estimation algorithm for HEVC is proposed to improve the PZB detection rate. Experimental results on the HM platform show that the proposed method saves about 50% of the rate-distortion optimization (RTO) time, with negligible Bjøntegaard delta bit rate loss. Our method is faster than other state-of-the-art ZB detection methods for HEVC by 10%-30%.