Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A low complexity wavelet based image coding algorithm for color image compression is presented. The key renovation of this algorithm is that a small number of symbol set was designed to convert the information from the wavelet transform domain into a compact data structure for each subband. The scheme works first by color space conversion, follod by uniform scalar quantization and data conversion where raster scanning order is performed for individual subband and quantized coefficients are converted into the symbol stream according to the designed symbol representation. Two different context lists in each subband are specified from the symbol stream and alternatively compressed by an adaptive arithmetic coder with high efficiency. Unlike zerotree coding or its variations which utilize the intersubband relationship into its own data representation, our work is a low complexity intrasubband based coding method which only addresses the information within the subband. The only extension is the termination symbols which carry the zero value information towards the end of the subband or across the subbands till the end of the image. Compared with the zerotree refined schemes, this algorithm results in competitive PSNR values and perceptually high quality images at the same compression ratio for color image compression.
['Min-Jen Tsai']
I am writing a research paper on a idea Very low bit rate color image compression by using stack-run-end coding
[{"from": "human", "value": "I am writing a research paper on a idea Very low bit rate color image compression by using stack-run-end coding"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A low complexity wavelet based image coding algorithm for color image compression is presented. The key renovation of this algorithm is that a small number of symbol set was designed to convert the information from the wavelet transform domain into a compact data structure for each subband. The scheme works first by color space conversion, follod by uniform scalar quantization and data conversion where raster scanning order is performed for individual subband and quantized coefficients are converted into the symbol stream according to the designed symbol representation. Two different context lists in each subband are specified from the symbol stream and alternatively compressed by an adaptive arithmetic coder with high efficiency. Unlike zerotree coding or its variations which utilize the intersubband relationship into its own data representation, our work is a low complexity intrasubband based coding method which only addresses the information within the subband. The only extension is the termination symbols which carry the zero value information towards the end of the subband or across the subbands till the end of the image. Compared with the zerotree refined schemes, this algorithm results in competitive PSNR values and perceptually high quality images at the same compression ratio for color image compression."}]