Object recognition tasks are commonplace in industrial and medical applications, but resource-limited end devices common to the Internet of Things (IoT) are limited in capabilities and commonly require offloading of compute-intensive tasks. We evaluate the negative latency concept as a new approach to image detection and object recognition applications in low-bandwidth scenarios without a feedback channel. We employ image quality assessment and risk estimators associated with image quality degradation for progressively transmitted images to realize negative latency. Our comprehensive evaluation on image recognition and object detection tasks shows that image quality assessment over progressively transmitted images can be enabled by using risk estimators associated with image quality degradation during progressive transmission. In turn, our approach provides the framework for service latency and resource use reductions.