• Content-adaptive video encryption in the compressed domain • Dynamically selects syntax elements based on video content complexity • Maintains full format compliance using Adaptive Syntax Integrity (ASI) • Tunable parameters balance encryption strength and bitrate increase With the ever-increasing amount of digital video content, efficient encryption is crucial to protect visual content across diverse platforms. Existing methods often incur excessive bitrate overhead due to content variability. Furthermore, since most videos are already compressed, encryption in the compressed domain is essential to avoid processing overhead and re-compression quality loss. However, achieving both format compliance and compression efficiency while ensuring that the decoded content remains unrecognizable is challenging in the compressed domain, since only limited information is available without full decoding. This paper proposes an adaptive compressed domain video encryption (ACDC) method that dynamically adjusts the encryption strategy according to content characteristics. Two tunable parameters derived from the bitstream information enable adaptation to various application requirements. An adaptive syntax integrity method is employed to produce format-compliant bitstreams without full decoding. Experimental results show that ACDC reduces bitrate overhead by 48.2% and achieves a 31-fold speedup in encryption time compared to the latest state of the art, while producing visually unrecognizable outputs.

The push toward data-driven video processing, combined with recent advances in video coding and streaming technologies, has fueled the need for diverse, large-scale, and high-quality video datasets. However, the limited availability of such datasets remains a key barrier to the development of next-generation video processing solutions. In this paper, we introduce Nature-1k, a large-scale video dataset consisting of 1000 professionally captured 4K Ultra High Definition (UHD) videos, each recorded at 60fps. The dataset covers a wide range of environments, lighting conditions, texture complexities, and motion patterns. To maintain temporal consistency, which is crucial for spatio-temporal learning applications, the dataset avoids scene cuts within the sequences. We further characterize the dataset using established metrics, including spatial and temporal video complexity metrics, as well as colorfulness, brightness, and contrast distribution. Moreover, Nature-1k includes a compressed version to support rapid prototyping and lightweight testing. The quality of the compressed videos is evaluated using four commonly used video quality metrics: PSNR, SSIM, MS-SSIM, and VMAF. Finally, we compare Nature-1k with existing datasets to demonstrate its superior quality and content diversity. The dataset is suitable for a wide range of applications, including Generative Artificial Intelligence (AI), video super-resolution and enhancement, video interpolation, as well as video coding, and adaptive video streaming optimization. Dataset URL: https://cd-athena.github.io/Nature-1k.

Video streaming now accounts for the majority of global internet traffic, making efficiency in both compression and energy consumption a critical challenge. Despite advancements in video coding and adaptive streaming, most methods remain content-agnostic and fail to fully exploit the unique characteristics of video content. This doctoral study focuses on encoder-side optimizations to develop content-adaptive methods that improve both energy and compression efficiency. This research integrates bitrate ladder optimization with representation-level enhancements to enable more energy-efficient streaming while maintaining high video quality.

With the ubiquity of video streaming, optimizing the delivery of video content while reducing energy consumption has become increasingly critical. Traditional adaptive streaming relies on a fixed set of bitrate-resolution pairs, known as bitrate ladders, for encoding. However, this "one-size-fits-all" approach is suboptimal for diverse video content. As a result, per-title encoding approaches dynamically select the bitrate ladder for each content. In this paper, we address the pressing issue of increasing energy consumption in video streaming by introducing GreenRes, a novel approach that goes beyond the traditional selection of quality-centric resolutions. Instead, GreenRes considers both video quality and energy consumption to construct an optimal bitrate ladder tailored to the unique characteristics of each video content. To achieve this, GreenRes, similar to per-title encoding, encodes each video content at various resolutions, each with a set of bitrates. It then sets a maximum acceptable quality drop threshold and selects resolutions that maintain video quality above this threshold while minimizing energy consumption. Our experimental results demonstrate an average reduction in energy consumption of 30.82%, while ensuring a maximum quality drop of only 0.53 Video Multimethod Assessment Fusion (VMAF) points.

The optimization of video compression and streaming workflows critically relies on understanding the video complexity, including both spatial and temporal features. These features play a vital role in guiding rate control, predicting video encoding parameters (such as resolution and frame rate), and selecting test videos for subjective analysis. Traditional methods primarily utilize Spatial Information (SI) and Temporal Information (TI) to measure these spatial and temporal complexity features, respectively. Moreover, the Video Complexity Analyzer (VCA) has been introduced as a tool employing Discrete Cosine Transform (DCT)-based functions, namely E and h, to evaluate the spatial and temporal complexity features, respectively. In this paper, we introduce the Enhanced Video Complexity Analyzer (EVCA), an advanced tool that integrates the functionalities of both VCA and the SITI approach. Developed in Python to ensure compatibility with GPU processing, EVCA enhances the definition of temporal complexity originally used in VCA. This refinement significantly improves the detection of temporal complexity features in VCA (i.e., h), raising its Pearson Correlation Coefficient (PCC) from 0.6 to 0.77. Furthermore, EVCA demonstrates exceptional performance on Graphics Processing Unit (GPU) devices, achieving feature extraction speeds exceeding 1200 fps for 1080p resolution videos.

In the field of video streaming, the optimization of video encoding and decoding processes is crucial for delivering high-quality video content. Given the growing concern about carbon dioxide emissions, it is equally necessary to consider the energy consumption associated with video streaming. Therefore, to take advantage of machine learning techniques for optimizing video delivery, a dataset encompassing the energy consumption of the encoding and decoding process is needed. This paper introduces a comprehensive dataset featuring diverse video content, encoded and decoded using various codecs and spanning different devices. The dataset includes 1000 videos encoded with four resolutions (2160p, 1080p, 720p, and 540p) at two frame rates (30fps and 60fps), resulting in eight unique encodings for each video. Each video is further encoded with four different codecs — AVC (libx264), HEVC (libx265), AV1 (libsvtav1), and VVC (VVenC) — at four quality levels defined by QPs of 22, 27, 32 and 37. In addition, for AV1, three additional QPs of 35, 46 and 55 are considered. We measure both encoding and decoding time and energy consumption on various devices to provide a comprehensive evaluation, employing various metrics and tools. Additionally, we assess encoding bitrate and quality using quality metrics such as PSNR, SSIM, MS-SSIM, and VMAF. All data and the reproduction commands and scripts have been made publicly available as part of the dataset, which can be used for various applications such as rate and quality control, resource allocation, and energy-efficient streaming.Dataset URL: https://github.com/cd-athena/MVCD.

This article investigates the relative performance of multiple data embedding into H.264 compressed video under two schemes of closed-loop and open-loop methods. In closed-loop, a part of an encoder is modified to embed data within the encoding loop during re-compression, while in the open-loop scenario, a part of decoder is modified to embed data out of decoding loop. It is shown, while for the first instance of embedding, both methods behave almost similarly for all picture types, for later instances of embedding, open-loop outperforms the closed-loop method. Moreover, the required time for embedding and extracting processes of open-loop method is only 1-1.5% of that of the closed-loop counterpart. In both methods, the quality of watermarked video and the data hiding capacity are controlled by the position of last non-zero (LNZ) coefficient in the H.264 zigzag scanning order. However, for B-pictures, the side-effect of embedding distortion is very limited (in the order of 0.002 in terms of SSIM), but for I- and P-pictures it can be significant. Picture degradations in I- and P-frames are alleviated by confining data embedding only into the last block of macroblocks of I-frames and the last P-frame in the GOP, respectively. Finally, it is shown that while with CAVLC type entropy coder, the number of increased bits due to data embedding can vary from 5%-65% of the metadata volume (depending on quality degradation) this value with CABAC coder is only less than 20% of that under CAVLC.