As video data is increasingly consumed by machines rather than solely by humans, there is a growing demand for new compression methods that efficiently accommodate this shift. Since object information extracted from videos is crucial for machine consumption, analyzing the similarity between adjacent pictures based on tracked object information can help identify motion-based redundancy which can then be utilized for video compression. In this paper, we perform object tracking on input video and analyze the similarity between adjacent pictures based on the movement of tracked objects. After classifying the pictures based on their similarity, highly redundant pictures within each group are aggressively resampled in the temporal domain to improve compression efficiency while maintaining machine performance. We propose a novel picture grouping method to cluster similar adjacent pictures and describe the process of similarity assessment. We evaluated the compression efficiency of the proposed object tracking-based adaptive temporal resampling through performance evaluation experiments, achieving BD-mAP improvements in object detection of 1.29%, 0.47%, and 2.44% for Random Access (RA), Low-Delay (LD), and All Intra (AI) modes, respectively, and achieving BD-MOTA improvements in object tracking of 0.02%, 0.61%, and 6.86% for RA, LD, and AI modes, respectively.

Volumetric display technologies continue to advance, accompanied by increasing complexity in volumetric media services, particularly for XR (eXtended Reality) mobile devices. This trend underscores the need for efficient compression and transmission of volumetric video. In response, ISO/IEC JTC1/SC29 WG7 is developing a standard for mesh-based volumetric media, known as Video-based Dynamic Mesh Coding (V-DMC). V-DMC reduces geometry bitstream size through lossy mesh decimation and compensates for over-simplified geometry using mesh subdivision and displacement generation, which includes an adaptive face-and-edge update process. However, the existing subdivision framework often struggles to recover fine surface details due to rigid subdivision criteria and unintended mesh separations in geometrically irregular regions. This leads to visible artifacts-especially under dynamic lighting and viewpoint changes common in XR environments. To address these issues, this paper analyzes key performance bottlenecks and introduces several implementation strategies to improve both compression efficiency and visual fidelity through an enhanced adaptive subdivision framework. The proposed method introduces Level-of-Detail (LoD)-aware subdivision to improve the flexibility and effectiveness of the adaptive face-and-edge update. It also incorporates regional feature-aware adaptivity to mitigate geometric distortions, particularly in narrow or topologically complex regions. Experimental results using MPEG-I test content demonstrate a$10-20 \%$reduction in displacement bitstream size and BD-rate gains of$2-5 \%$compared to the V-DMC Test Model (TMM v12.0). These results confirm that the proposed method shows promise in enhancing both visual fidelity and compression efficiency, potentially contributing to the fulfillment of the stringent requirements of modern 3D rendering.

Conventional video coding methods have been developed based on the human visual system (HVS). However, in recent years, video has occupied a huge portion of internet traffic, and the mount of video data for machine consumption has increased rapidly due to the progress of neural networks. This paper proposes a novel machine-attention-based video coding method for machines. Inspired by the saliency-driven research, we first extract attention regions, sensitively affecting the machine vision performance, from the object detection network. Subsequently, a maximum a posterior (MAP)-based bit allocation method is applied to assign more bits to the attention regions. Our proposed method helps to maintain high machine vision performance whereas reducing the bitrate. Experimental results show that our proposed method achieves up to 34.89% bjøntegaard delta (BD)-rate reduction for the video dataset and up to 44.70% BD-rate reduction for the image dataset compared to state-of-the-art video coding technology.

This paper describes experimental demonstration of holographic 3D reconstructions with a directly-viewing, transmissive, amplitude-modulating 5.5″ LCD SLM device in monochrome. A full-complex field modulation is achieved by the single amplitude-modulation SLM module using the Burckhardt-encoding scheme at the cost of the decrease in sampling. We define an effective holographic contrast ratio for the first time in this work. We further report that the effective contrast ratio from Burckhardt-encoded hologram is superior to one from conventional amplitude-only encoded hologram.

This paper presents ISO/IEC 23000-5 -media streaming player, an ISO standard targeting the distribution of governed content in streaming mode. This standard is a major integration of MPEG technologies, ranging from MPEG-2 and MPEG-4 to MPEG-21 including a set of inter-device protocols developed by the Digital Media Project, now ISO/IEC 29116-1 -Media Streaming MAF Protocols. The paper also presents Chillout, adopted for the open source reference software of this standard. Chillout is written in Java and provides a set of libraries for developing applications conforming to the standard.

Reversible data embedding theory has marked a new epoch for data hiding and information security. Being reversible, the original data and the embedded data should be completely restored. Difference expansion transform is a remarkable breakthrough in reversible data-hiding schemes. The difference expansion method achieves high embedding capacity and keeps distortion low. This paper shows that the difference expansion method with the simplified location map and new expandability can achieve more embedding capacity while keeping the distortion at the same level as the original expansion method. Performance of the proposed scheme in this paper is shown to be better than the original difference expansion scheme by Tian and its improved version by Kamstra and Heijmans. This improvement can be possible by exploiting the quasi-Laplace distribution of the difference values.