Emerging single-photon camera (SPC) technologies have unique challenges in data acquisition and processing. Unlike conventional sensors that produce a single 8- to 16-bit brightness value per pixel, SPCs record photon arrivals with many more samples per pixel, using high floating-point precision for each photon collected. This means that they must handle potentially millions of timestamps, especially at higher spatial resolutions and in the presence of ambient light, creating bottlenecks within the pixel circuitry. To address these challenges associated with SPCs, this paper proposes adaptive algorithms designed to efficiently distribute hardware resources among groups of pixels. By selectively subsampling the data entering the sensor, the proposed approach significantly reduces system load and opens new paths toward high-quality HDR imaging using SPCs at high spatial resolutions.

Traditional video compression continues to advance, but the gains in efficiency are diminishing and come at the cost of higher computational complexity. Despite achieving competitive rate-distortion results, current neural video codecs (NVCs) generally lack support for a wide range of quality levels, often requiring multiple models to achieve flexible rate control, which increases both training cost and deployment complexity. To address the limitations of both traditional codecs and current NVCs, we propose a hybrid video compression framework that integrates traditional codecs with hint-guided video frame interpolation (VFI), a learning-based technique for synthesizing intermediate frames. By using decoded reference frames and leveraging compressed-domain hints to guide interpolation, our method improves both motion compensation and reconstruction quality. This design combines the efficiency of traditional codecs with the adaptability of neural interpolation, achieving consistent rate-distortion performance and supporting a wide range of quality levels on standard benchmarks.

Video data is ubiquitous; capturing, transferring, and storing even compressed video data is challenging because it requires substantial resources. With the large amount of video traffic being transmitted on the internet, any improvement in compressing such data, even small, can drastically impact resource consumption. In this paper, we present a hybrid video compression framework that unites the advantages of both DCT-based and interpolation-based video compression methods in a single framework. We show that our work can deliver the same visual quality or, in some cases, improve visual quality while reducing the bandwidth by 10--20%.