VidTok is an innovative approach to video processing that reduces redundancy while preserving essential information. It converts raw video data into compact tokens, making storage, compression, and reconstruction more efficient. By applying convolutional encoders, Finite Scalar Quantization (FSQ), and temporal blending, VidTok focuses on meaningful changes while minimizing unnecessary processing. This approach makes tasks like video generation, editing, and retrieval more efficient without sacrificing important details. The model achieves strong performance on video reconstruction benchmarks, with a Peak Signal-to-Noise Ratio (PSNR) of 29.82 dB and a Structural Similarity Index (SSIM) of 0.867. VidTok provides an efficient approach to video representation, improving retrieval, storage, and generation, and is poised for further improvements in motion tracking, multi-scale encoding, and adaptive quantization.