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Point Clouds (PCs) have gained significant attention due to their usage in diverse application domains, notably virtual and augmented reality. While PCs excel in providing detailed 3D visualization, this typically requires millions of points which must be efficiently coded for real-world deployment, notably storage and streaming. Recently, learning-based coding solutions have been adopted, notably in the JPEG Pleno Point Coding (PCC) standard, which uses a coding model with millions of model parameters.

Implicit Neural Representations (INR) are a novel data representation technique which is gaining ground in the image compression field due to its simplicity and interesting results in terms of rate/distortion ratio.

Vectorized high-definition (HD) map construction is an important and challenging task for autonomous driving. End-to-end models have been developed recently to enable online map construction. Existing works have difficulty in generating complex geometric shapes and lack comprehensive evaluation metrics. To tackle these challenges, we introduce buffered IoU as a novel metric for vectorized map construction, which is clearly defined and applicable to real-world situations. Inspired by methods of rotated object detection, we further propose a novel technique called Buffered Gaussian Modeling.

Hybrid image retrieval is a significant task for a wide range of applications. In this scenario, the hybrid query for searching images consists of a reference image and a text modifier. The reference image provides a vital visual context and displays some semantic details, while the text modifier specifies the modifications to the reference image. To address such hybrid cross-modal retrieval, we propose a multi-level contrastive learning (MLCL) method for combining the hybrid query features into a fused feature by cross-modal contrastive learning with multi-level semantic alignment.

Versatile Video Coding (VVC) now supports Screen Content Coding (SCC) by integrating two efficient coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the numerous
modes and the Quad-Tree Plus Multi-Type Tree (QTMT) structure inherent to VVC contribute to a very high coding complexity. To effectively reduce the computational complexity
of VVC SCC, we propose a fast Intra mode prediction algorithm for VVC SCC. More specifically, we first use the difference of minimum Sum of Absolute Transformed Differences

Previous optical flow based video compression is gradually replaced by unsupervised deformable convolution (DCN) based method. This is mainly due to the fact that the motion vector (MV) estimated by the existing optical flow network

The last standard Versatile Video Codec (VVC) aims to improve the compression efficiency by saving around 50% of bitrate at the same quality compared to its predecessor High Efficiency Video Codec (HEVC). However, this comes with higher encoding complexity mainly due to a much larger number of block splits to be tested on the encoder side.

End-to-end image compression has achieved satisfactory results in recent years. However, existing methods suffer from the high complexity of complicated neural networks and cannot be directly deployed on mobile devices due to the limitations of computation and storage. Therefore, considering the resource and computing ability constrains of the mobile devices, we make a trade-off in this paper between rate-distortion (R-D) performance, inference time, and model complexity.

Visual content is increasingly being used for more than human viewing. For example, traffic video is automatically analyzed to count vehicles, detect traffic violations, estimate traffic intensity, and recognize license plates; images uploaded to social media are automatically analyzed to detect and recognize people, organize images into thematic collections, and so on; visual sensors on autonomous vehicles analyze captured signals to help the vehicle navigate, avoid obstacles, collisions, and optimize their movement.

This supplementary material presents detailed experimental results for ICIP 2024 submission titled "Standard Compliant Video Coding using Low Complexity, Switchable Neural Wrappers".