Modern media data such as 360 videos and light field (LF) images are typically captured in much higher dimensions than the observers' visual displays. To efficiently browse high-dimensional media over bandwidth-constrained networks, a navigational streaming model is considered: a client navigates the large media space by dictating a navigation path to a server, who in response transmits the corresponding pre-encoded media data units (MDU) to the client one-by-one in sequence. Intra-coding an MDU (I-MDU) would result in a large bitrate but I-MDU can be randomly accessed, while inter-coding an MDU (P-MDU) using another MDU as a predictor incurs a small coding cost but imposes an order where the predictor must be first transmitted and decoded. From a compression perspective, the technical challenge is: how to achieve coding gain via inter-coding of MDUs, while enabling adequate random access for satisfactory user navigation. To address this problem, we propose landmarks, a selection of key MDUs from the high-dimensional media. Using landmarks as predictors, nearby MDUs in local neighborhoods are intercoded, resulting in a predictive MDU structure with controlled coding cost. It means that any requested MDU can be decoded by at most transmitting a landmark and an inter-coded MDU, enabling navigational random access. To build a landmarked MDU structure, we employ tree-structured vector quantizer (TSVQ) to first optimize landmark locations, then iteratively add/remove inter-coded MDUs as refinements using a fast branch-and-bound technique. Taking interactive LF images and viewport adaptive 360 images as illustrative applications, and I-, P- and previously proposed merge frames to intra- and inter-code MDUs, we show experimentally that landmarked MDU structures can noticeably reduce the expected transmission cost compared with MDU structures without landmarks.

中文翻译：

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存储的高维媒体导航流的地标

现代媒体数据（例如360视频和光场（LF）图像）通常以比观察者的视觉显示更高的维度捕获。为了在带宽受限的网络上有效浏览高维媒体，需要考虑一种导航流模型：客户端通过指示到服务器的导航路径来导航大型媒体空间，而服务器则将相应的预编码媒体数据单元（MDU）发送给服务器。 ）依次向客户发送）。对MDU（I-MDU）进行帧内编码会导致较大的比特率，但I-MDU可以随机访问，而使用另一个MDU作为预测变量对MDU（P-MDU）进行帧间编码会产生较小的编码成本，但会增加必须先发送和解码预测变量的顺序。从压缩的角度来看，技术挑战是：如何通过MDU的互编码实现编码增益，同时实现足够的随机访问以实现令人满意的用户导航。为了解决这个问题，我们提出了界标，即从高维媒体中选择关键的MDU。使用地标作为预测因子，对本地邻域中附近的MDU进行互编码，从而得到具有受控编码成本的预测MDU结构。这意味着最多可以通过发送界标和帧间编码的MDU来解码任何请求的MDU，从而实现导航随机访问。为了建立具有里程碑意义的MDU结构，我们采用树结构矢量量化器（TSVQ）首先优化地标位置，然后使用快速分支定界技术迭代地添加/删除帧间编码的MDU作为细化。以交互式LF图像和视口自适应360图像为例，I-，