Spiking Neural Networks (SNNs) offer a promising avenue for energy-efficient computations on neuromorphic hardware, leveraging the unique advantages of spike-based signaling. Despite their potential, SNNs often lag behind Artificial Neural Networks (ANNs) in performance, mainly due to the complexity of effectively translating ANN activation values into the time domain spikes. This challenge introduces quantization and unevenness errors, which arise from mapping continuous activations to discrete spikes and irregular spike timing. This paper introduces NeuBridge, an innovative ANN-SNN conversion method that utilize temporal coding to significantly reduce the number of required timesteps without compromising accuracy. NeuBridge addresses these errors by employing a decode-encode neuron and adaptive temporal coding, effectively bridging the performance gap between ANNs and SNNs. By establishing an equivalence between quantized ANNs and SNNs and optimizing the temporal coding process, we improve SNN performance with as few as 2 timesteps. Empirical evaluations on CIFAR-10 and ImageNet datasets demonstrate that NeuBridge consistently performs better than existing conversion methods in both accuracy and efficiency, achieving high performance within just 3 timesteps.