Data acquired over long periods of time like High Definition (HD) videos or records from a sensor over long time intervals, have to be efficiently compressed, to reduce their size. The compression has also to allow efficient access to random parts of the data upon request from the users. Efficient compression is usually achieved with prediction between data points at successive time instants. However, this creates dependencies between the compressed representations, which is contrary to the idea of random access. Prediction methods rely in particular on reference data points, used to predict other data points. The placement of these references balances compression efficiency and random access. Existing solutions to position the references use ad hoc methods. In this paper, we study this joint problem of compression efficiency and random access. We introduce the storage cost as a measure of the compression efficiency and the transmission cost for the random access ability. We express the reference placement problem that trades storage with transmission cost as an integer linear programming problem. Considering additional assumptions on the sources and coding methods reduces the complexity of the search space of the optimization problem. Moreover, we show that the classical periodic placement of the references is optimal, when the encoding costs of each data point are equal and when requests of successive data points are made. In this particular case, a closed-form expression of the optimal period is derived. Finally, the proposed optimal placement strategy is compared with an ad hoc method, where the references correspond to sources where the prediction does not help reducing significantly the encoding cost. The proposed optimal algorithm shows a bit saving of −20% with respect to the ad hoc method.

中文翻译：

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预测编码方案中随机访问的最佳参考选择

长时间获取的数据（如高清 (HD) 视频或传感器长时间间隔的记录）必须有效压缩，以减小其大小。压缩还必须允许在用户请求时有效访问数据的随机部分。通常通过在连续时间点的数据点之间进行预测来实现有效的压缩。然而，这会在压缩表示之间产生依赖关系，这与随机访问的想法相反。预测方法特别依赖于用于预测其他数据点的参考数据点。这些引用的放置平衡了压缩效率和随机访问。定位参考的现有解决方案使用特别方法。在本文中，我们研究了压缩效率和随机访问的联合问题。我们引入存储成本作为随机访问能力的压缩效率和传输成本的衡量标准。我们将存储与传输成本交换的参考放置问题表示为整数线性规划问题。考虑对来源和编码方法的额外假设降低了优化问题的搜索空间的复杂性。此外，我们表明，当每个数据点的编码成本相等并且发出连续数据点的请求时，参考的经典周期性放置是最佳的。在这种特殊情况下，将推导出最优周期的闭式表达式。最后，将所提出的最优放置策略与一种特别的方法进行比较，其中参考对应于预测无助于显着降低编码成本的源。所提出的最优算法显示出相对于 ad hoc 方法节省了 -20% 的位。