Image, Video, and Multidimensional Signal Processing

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Video compression standards rely heavily on eliminating spatial and temporal redundancy within and across video frames. Intra-frame encoding targets redundancy within blocks of a single video frame, whereas inter-frame coding focuses on removing redundancy between the current frame and its reference frames.

Omnidirectional visual content, commonly referred to as 360-degree images and videos, has garnered significant interest in both academia and industry, establishing itself as the primary media modality for VR/XR applications. 

View synthesis is a task of generating novel views of a scene/object from a given set of input views. It is a challenging and important problem in computer vision and graphics, with significant applications in virtual and augmented reality, 3D reconstruction, video editing, and more.

Supported by the SPS Challenge Program

The George B. Moody PhysioNet Challenges are annual competitions that invite participants to develop automated approaches for addressing important physiological and clinical problems. The 2024 Challenge invites teams to develop algorithms for digitizing and classifying electrocardiograms (ECGs) captured from images or paper printouts. 

Speech-enabled systems often experience performance degradation in real-world scenarios, primarily due to adverse acoustic conditions and interactions among multiple speakers. Enhancing the front-end speech processing technology is vital for improving the performance of the back-end systems. 

Introducing ICASSP 2024 SPGC competition aiming at reconstructing skin spectral reflectance in the visible (VIS) and near-infrared (NIR) spectral range from RGB images captured by everyday cameras, offering a transformative approach for cosmetic and beauty applications. 

In the ICIP 2023 Grand Challenge entitled "Automatic Detection of Mosquito Breeding Grounds", we consider the development of a video-analysis system for the automatic detection of objects commonly associated with mosquito foci: discarded tires, water tanks, buckets, puddles, pools, and bottles.

The proliferation of Unmanned Aerial Vehicles (UAVs) such as drones has caused serious security and privacy concerns in the recent past. Detecting drones is extremely challenging in conditions where the drones exactly resemble a bird or any other flying entity and are to be detected under low visibility conditions.

This challenge will perform the first comprehensive benchmark of the impact of a wide range of distortions on the performance of current object detection methods. The proposed database contains, in addition to the conventional real distortions, other synthesized photo-realistic distortions corresponding to real and very frequent scenarios often neglected in other databases despite their importance. The synthetic distortions are generated according to several types and severity levels with respect to the scene context.

Point clouds (PC) are widely used for storing and transmitting 3D visual data in applications like virtual reality, autonomous driving, etc. To deal with the large size and complexity of point clouds, efficient compression methods have been developed, including MPEG standards and recent deep-learning-based approaches. To optimize and benchmark processing algorithms and codecs, point cloud quality metrics are crucial.


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