Dependent scalar quantization with substitution in neural image compression

An apparatus for end-to-end neural image compression using dependent scalar quantization with substitution, the apparatus comprising:

• at least one memory configured to store program code;
• and
• at least one processor configured to read the program code and operate as instructed by the program code, the program code comprising:
• first receiving code configured to cause the at least one processor to receive an input image;
• first determining code configured to cause the at least one processor to determine a substitute image based on the input image using a neural network based substitute feature generator;
• compressing code configured to cause the at least one processor to compress the substitute image;
• quantizing code configured to cause the at least one processor to quantize the compressed substitute image to obtain a quantized representation of the input image with higher compression performance by using a first dependent scalar quantizer, and
• first generating code configured to cause the at least one processor to entropy encode the substitute image using a neural network based encoder to generate a compressed representation of the quantized representation,
• wherein a compression quality loss associated with the compressed substitute image is less than a compression quality loss associated with a compressed input image, and
• wherein one or more neural network models used in the method may be jointly optimized to minimize an overall rate-distortion loss.

A non-transitory computer readable medium storing instructions that, when executed by at least one processor for end-to-end neural image compression using dependent scalar quantization with substitution, cause the at least one processor to:

• receive an input image;
• determine a substitute image based on the input image using a neural network based substitute feature generator;
• compress the substitute image;
• quantize the compressed substitute image to obtain a quantized representation of the input image with higher compression performance by using a first dependent scalar quantizer, and
• entropy encode the substitute image using a neural network based encoder to generate a compressed representation of the quantized representation,
• wherein a compression quality loss associated with the compressed substitute image is less than a compression quality loss associated with a compressed input image, and
• wherein one or more neural network model used in the method may be jointly optimized to minimize an overall rate-distortion loss.