Explicit Residual-Based Scalable Image Coding for Humans and Machines

Scalable image compression is a technique that progressively reconstructs multiple versions of an image for different requirements. In recent years, images have increasingly been consumed not only by humans but also by image recognition models. This shift has drawn growing attention to scalable image compression methods that serve both machine and human vision (ICMH). Many existing models employ neural network-based codecs, known as learned image compression, and have made significant strides in this field by carefully designing the loss functions. In some cases, however, models are overly reliant on their learning capacity, and their architectural design is not sufficiently considered. In this paper, we enhance the coding efficiency and interpretability of ICMH framework by integrating an explicit residual compression mechanism, which is commonly employed in resolution scalable coding methods such as JPEG2000. Specifically, we propose two complementary methods: Feature Residual-based Scalable Coding (FR-ICMH) and Pixel Residual-based Scalable Coding (PR-ICMH). These proposed methods are applicable to various machine vision tasks. Moreover, they provide flexibility to choose between encoder complexity and compression performance, making it adaptable to diverse application requirements. Experimental results demonstrate the effectiveness of our proposed methods, with PR-ICMH achieving up to 29.57% BD-rate savings over the previous work.

Efficient Neural Video Representation via Structure-Preseving Patch Decoding

Implicit Neural Representations (INRs) have attracted significant interest for their ability to model complex signals by mapping spatial and temporal coordinates to signal values. In the context of neural video representation, several decoding strategies have been explored to balance compactness and reconstruction quality, including pixel-wise, frame-wise, and patch-wise methods. Patch-wise decoding aims to combine the flexibility of pixel-based models with the efficiency of frame-based approaches. However, conventional uniform patch division often leads to discontinuities at patch boundaries, as independently reconstructed regions may fail to form a coherent global structure. To address this limitation, we propose a neural video representation method based on Structure-Preserving Patches (SPPs). Our approach rearranges each frame into a set of spatially structured patch frames using a PixelUnshuffle-like operation. This rearrangement maintains the spatial coherence of the original frame while enabling patch-level decoding. The network learns to predict these rearranged patch frames, which supports a global-to-local fitting strategy and mitigates degradation caused by upsampling. Experiments on standard video datasets show that the proposed method improves reconstruction quality and compression performance compared to existing INR-based video representation methods.

SR-NeRV: Improving Embedding Efficiency of Neural Video Representation via Super-Resolution

Implicit Neural Representations (INRs) have garnered significant attention for their ability to model complex signals across a variety of domains. Recently, INR-based approaches have emerged as promising frameworks for neural video compression. While conventional methods primarily focus on embedding video content into compact neural networks for efficient representation, they often struggle to reconstruct high-frequency details under stringent model size constraints, which are critical in practical compression scenarios. To address this limitation, we propose an INR-based video representation method that integrates a general-purpose super-resolution (SR) network. Motivated by the observation that high-frequency components exhibit low temporal redundancy across frames, our method entrusts the reconstruction of fine details to the SR network. Experimental results demonstrate that the proposed method outperforms conventional INR-based baselines in terms of reconstruction quality, while maintaining comparable model sizes.

Adapting Image-to-Video Diffusion Models for Large-Motion Frame Interpolation

The development of video generation models has advanced significantly in recent years. For video frame interpolation, we adopt a pre-trained large-scale image-to-video diffusion model. To enable this adaptation, we propose a conditional encoder, which serves as a simple yet effective trainable module. By leveraging the first and last frames, we extract spatial and temporal features and input them into the conditional encoder. The computed features of the conditional encoder guide the video diffusion model in generating keyframe-guided video sequences. Our method demonstrates superior performance on the Fr\'echet Video Distance (FVD) metric compared to previous deterministic approaches in handling large-motion cases, highlighting advancements in generative-based methodologies.

Time Step Generating: A Universal Synthesized Deepfake Image Detector

Currently, high-fidelity text-to-image models are developed in an accelerating pace. Among them, Diffusion Models have led to a remarkable improvement in the quality of image generation, making it vary challenging to distinguish between real and synthesized images. It simultaneously raises serious concerns regarding privacy and security. Some methods are proposed to distinguish the diffusion model generated images through reconstructing. However, the inversion and denoising processes are time-consuming and heavily reliant on the pre-trained generative model. Consequently, if the pre-trained generative model meet the problem of out-of-domain, the detection performance declines. To address this issue, we propose a universal synthetic image detector Time Step Generating (TSG), which does not rely on pre-trained models' reconstructing ability, specific datasets, or sampling algorithms. Our method utilizes a pre-trained diffusion model's network as a feature extractor to capture fine-grained details, focusing on the subtle differences between real and synthetic images. By controlling the time step t of the network input, we can effectively extract these distinguishing detail features. Then, those features can be passed through a classifier (i.e. Resnet), which efficiently detects whether an image is synthetic or real. We test the proposed TSG on the large-scale GenImage benchmark and it achieves significant improvements in both accuracy and generalizability.

Classification in Japanese Sign Language Based on Dynamic Facial Expressions

Sign language is a visual language expressed through hand movements and non-manual markers. Non-manual markers include facial expressions and head movements. These expressions vary across different nations. Therefore, specialized analysis methods for each sign language are necessary. However, research on Japanese Sign Language (JSL) recognition is limited due to a lack of datasets. The development of recognition models that consider both manual and non-manual features of JSL is crucial for precise and smooth communication with deaf individuals. In JSL, sentence types such as affirmative statements and questions are distinguished by facial expressions. In this paper, we propose a JSL recognition method that focuses on facial expressions. Our proposed method utilizes a neural network to analyze facial features and classify sentence types. Through the experiments, we confirm our method's effectiveness by achieving a classification accuracy of 96.05%.

Inter-Feature-Map Differential Coding of Surveillance Video

In Collaborative Intelligence, a deep neural network (DNN) is partitioned and deployed at the edge and the cloud for bandwidth saving and system optimization. When a model input is an image, it has been confirmed that the intermediate feature map, the output from the edge, can be smaller than the input data size. However, its effectiveness has not been reported when the input is a video. In this study, we propose a method to compress the feature map of surveillance videos by applying inter-feature-map differential coding (IFMDC). IFMDC shows a compression ratio comparable to, or better than, HEVC to the input video in the case of small accuracy reduction. Our method is especially effective for videos that are sensitive to image quality degradation when HEVC is applied

Delta-ICM: Entropy Modeling with Delta Function for Learned Image Compression

Image Coding for Machines (ICM) is becoming more important as research in computer vision progresses. ICM is a vital research field that pursues the use of images for image recognition models, facilitating efficient image transmission and storage. The demand for recognition models is growing rapidly among the general public, and their performance continues to improve. To meet these needs, exchanging image data between consumer devices and cloud AI using ICM technology could be one possible solution. In ICM, various image compression methods have adopted Learned Image Compression (LIC). LIC includes an entropy model for estimating the bitrate of latent features, and the design of this model significantly affects its performance. Typically, LIC methods assume that the distribution of latent features follows a normal distribution. This assumption is effective for compressing images intended for human vision. However, employing an entropy model based on normal distribution is inefficient in ICM due to the limitation of image parts that require precise decoding. To address this, we propose Delta-ICM, which uses a probability distribution based on a delta function. Assuming the delta distribution as a distribution of latent features reduces the entropy of image portions unnecessary for machines. We compress the remaining portions using an entropy model based on normal distribution, similar to existing methods. Delta-ICM selects between the entropy model based on the delta distribution and the one based on the normal distribution for each latent feature. Our method outperforms existing ICM methods in image compression performance aimed at machines.

Neural Video Representation for Redundancy Reduction and Consistency Preservation

Implicit neural representations (INRs) embed various signals into networks. They have gained attention in recent years because of their versatility in handling diverse signal types. For videos, INRs achieve video compression by embedding video signals into networks and compressing them. Conventional methods use an index that expresses the time of the frame or the features extracted from the frame as inputs to the network. The latter method provides greater expressive capability as the input is specific to each video. However, the features extracted from frames often contain redundancy, which contradicts the purpose of video compression. Moreover, since frame time information is not explicitly provided to the network, learning the relationships between frames is challenging. To address these issues, we aim to reduce feature redundancy by extracting features based on the high-frequency components of the frames. In addition, we use feature differences between adjacent frames in order for the network to learn frame relationships smoothly. We propose a video representation method that uses the high-frequency components of frames and the differences in features between adjacent frames. The experimental results show that our method outperforms the existing HNeRV method in 90 percent of the videos.

Refining Coded Image in Human Vision Layer Using CNN-Based Post-Processing

Scalable image coding for both humans and machines is a technique that has gained a lot of attention recently. This technology enables the hierarchical decoding of images for human vision and image recognition models. It is a highly effective method when images need to serve both purposes. However, no research has yet incorporated the post-processing commonly used in popular image compression schemes into scalable image coding method for humans and machines. In this paper, we propose a method to enhance the quality of decoded images for humans by integrating post-processing into scalable coding scheme. Experimental results show that the post-processing improves compression performance. Furthermore, the effectiveness of the proposed method is validated through comparisons with traditional methods.