## 4 Opportunities and Open Research Areas in VANET Multimedia
### Batch Transmission and Reconstruction of Audio-Visual Information  
In DSRC VANETs, various types of messages are transmitted to facilitate communication among vehicles and infrastructure units. These messages include Basic Safety Messages (BSMs), Emergency Warning Messages, Traffic Information Messages (TIMs), Cooperative Awareness Messages (CAMs), Roadside-to-Vehicle Messages (RSMs), Infrastructure-to-Vehicle Messages (IVMs), Probe Data Messages, and vehicle-to-infrastructure Messages (VIMs). Each message serves a distinct purpose within the VANET context, encompassing safety-related information exchange, traffic management, and value-added services. These messages play a pivotal role in providing drivers with critical updates on traffic congestion, road closures, and other vital road conditions. However, to prevent interference with the bandwidth utilization of these essential messages, efficient image compression and batched transmission methods become imperative. Consequently, at the receiving end, the decompression of these batches becomes necessary. This approach is essential due to the adverse consequences that arise from transmitting large images in their entirety, such as network congestion and inefficient resource allocation. In the landscape of vehicular ad hoc networks (VANETs), where efficient data exchange is paramount, the conventional methods of image compression face unique challenges. These challenges arise due to the stringent bandwidth constraints and the need for real-time communication. In this context, it is imperative to explore innovative compression techniques that not only build upon established methods like JPEG that use Discrete Cosine Transform (DCT) to transform spatial image data into the frequency domain but also focus on other compression techniques [59]. One such groundbreaking approach worth investigating is the integration of two powerful signal processing techniques: Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) [60]. These techniques,renowned in the field of image processing, offer a novel perspective on image compression within VANETs. DWT excels in breaking down an image into multi-resolution components, capturing both frequency and spatial information simultaneously [61]. This inherent ability makes it well-suited for the diverse characteristics of vehicular images, which often exhibit variations in both content and resolution. Similarly, the SVD is renowned for its capacity to reduce image dimensionality while preserving essential image features. It achieves this by identifying and retaining the most significant singular values and their corresponding vectors. This selective preservation of information aligns with the goal of efficient image transmission in bandwidth-constrained VANETs. The batch transfer involves partitioning images into smaller packets for sequential transmission, thereby reducing congestion and efficiently utilizing network resources. Upon receipt of the image packets, the image-reassembly process is used to reconstruct the complete image using specific protocols or algorithms. By implementing these techniques, DSRC VANET devices may effectively compress images, transfer them in batches, and reassemble them at the receiving end. The need for faster image decompression on the receiving end is driven by several critical factors. Firstly, timeliness is of utmost importance in VANETs, where real-time communication is required to ensure effective decision-making and response. When compressed images are transmitted, it becomes crucial to decompress them quickly at the receiving end to maintain the timeliness of the information. Any delays in decompression can hinder the intended purpose of the image, such as conveying important road conditions or emergency alerts. Secondly, the user experience plays a significant role in VANET applications, which often involve visual content intended for presentation to vehicle occupants or road users. A smooth and seamless user experience relies on the timely delivery and decompression of images.