<p>The fast advancement of the multimedia era has led to an explosion in the use and technology of large amounts of digital snapshots. It has created a developing call for Image compression techniques that can reduce the dimensions of Image without compromising their perceptual excellence. Lossless picture compression techniques have emerged as a popular answer in this regard, as they maintain the precise statistics of the original Image. However, current lossless compression techniques face demanding situations in accomplishing high compression ratios without sacrificing the picture best. This paper proposes a more robust lossless Image compression framework based on the Tetrolet transformation. The framework introduces an adaptive quantization method, which adjusts the quantization step length based totally on the neighborhood Image characteristics, resulting in better compression overall performance for exclusive sorts of Image. Context modeling is also included in the framework to make the most of the inter-Image correlations inside the Image. This method uses context information from the Detroit-converted coefficients, which might be used to estimate the probability of prevalence for every Image value. The proposed model obtained 94.09% accuracy, 92.90% Precision, 95.25% Recall, 92.91% F1-Score and 93.87% Compression Duration. Using as it should be modeling these chances; the framework improves the entropy coding performance and ultimately results in better compression ratios.</p>