# US 2025/0097368 A1 · Complex color space conversion using convolutional neural networks

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- Number: US 2025/0097368 A1
- Document ID: US20250097368A1
- Status: Published application
- Kind: A1
- Application number: 18/898,257
- Filed: 2024-09-26
- Published: 2025-03-20
- Earliest claimed: 2023-09-18
- Assignees: KYOCERA Document Solutions Inc.
- Applicants: KYOCERA Document Solutions, Inc.
- Inventors: Michael M. Chang; Dongpei Su; Sheng Li; Kenneth Allen Schmidt
- Web page: https://patentgrep.com/patent/US20250097368A1

## CPC

- H04N 1/6016: {Conversion to subtractive colour signals}
- G06N 3/04: Architecture, e.g. interconnection topology
- G06N 3/045: Combinations of networks
- G06N 3/08: Learning methods
- H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A device such as a color printer includes a main memory, a cache memory, and a convolutional neural network configured to convert pixels from a first color space to a second color space. The convolutional neural network is organized into execution-separable layers, and loaded one or more layer at a time (depending on cache size) from the main memory to the cache memory, whereby the pixels are processed through each of the layers in the cache memory, and layers that have completed processing are evicted to make room for caching next layer(s) of the network.

## Claims

### Claim 1

A device comprising:
a main memory;
a cache memory;
one or more convolutional neural networks configured to convert a plurality of pixels from a source color space to a destination color space,
wherein the one or more convolutional neural networks utilize a source color space profile and a destination color space profile in sequence,
wherein the source color space profile and the destination color space profile are each an International Color Consortium (ICC) profile,
wherein the source color space is a display device color space and the destination color space is a printer device color space, and
wherein the one or more convolutional neural networks are organized into execution-separable layers; and
logic to load a first layer of the one or more convolutional neural networks from the main memory to the cache memory, process the pixels through the first layer, evict the first layer from the cache memory, load a second layer of the one or more convolutional neural networks from the main memory to the cache memory, and process the pixels through the second layer.

### Claim 2 (depends on 1)

The device of claim 1, wherein the one or more convolutional neural networks each comprise exactly six convolutional layers.

### Claim 3 (depends on 2)

The device of claim 2, wherein each of the six convolutional layers comprises a filter size of one.

### Claim 4 (depends on 3)

The device of claim 3, wherein each of the convolutional layers comprises a stride size of one.

### Claim 5 (depends on 2)

The device of claim 2, wherein the six convolutional layers are configured as 3->32->64->128->128->64->4.

### Claim 6 (depends on 2)

The device of claim 2, wherein the six convolutional layers are configured as 3->64->128->256->128->64->4.

### Claim 7 (depends on 1)

The device of claim 1, wherein the source color space and the destination color space are both three-dimensional color spaces or both four-dimensional color spaces.

### Claim 8 (depends on 1)

The device of claim 1, wherein one of the color spaces is a three-dimensional color space and the other color space is a four-dimensional color space.

### Claim 9 (depends on 1)

The device of claim 1, wherein the device is a color printer.

### Claim 10

A device comprising:
a main memory;
a cache memory;
a convolutional neural network configured to convert a plurality of pixels from a source color space to a destination color space,
wherein the convolutional neural network utilizes a single International Color Consortium (ICC) profile without an intermediate conversion, and
wherein the convolutional neural network is organized into execution-separable layers; and
logic to load a first layer of the convolutional neural network from the main memory to the cache memory, process the pixels through the first layer, evict the first layer from the cache memory, load a second layer of the convolutional neural network from the main memory to the cache memory, and process the pixels through the second layer.

### Claim 11 (depends on 10)

The device of claim 10, wherein the one or more convolutional neural networks each comprise exactly six convolutional layers.

### Claim 12 (depends on 11)

The device of claim 11, wherein each of the six convolutional layers comprises a filter size of one.

### Claim 13 (depends on 10)

The device of claim 10, wherein the source color space and the destination color space are both three-dimensional color spaces or both four-dimensional color spaces.

### Claim 14 (depends on 10)

The device of claim 10, wherein one of the color spaces is a three-dimensional color space and the other color space is a four-dimensional color space.

### Claim 15 (depends on 10)

The device of claim 10, wherein the device is a color printer.

### Claim 16

A device comprising:
at least one processor;
a main memory;
a cache memory;
a convolutional neural network configured to convert a plurality of pixels from a first color space to a second color space, the convolutional neural network organized into execution-separable layers; and
logic to load a first layer of the convolutional neural network from the main memory to the cache memory, process by the at least one processor, in the cache memory, the pixels through the first layer, evict the first layer from the cache memory, load a second layer of the convolutional neural network from the main memory to the cache memory, and process by the at least one processor, in the cache memory, the pixels through the second layer.

### Claim 17 (depends on 16)

The device of claim 16, wherein the at least one processor may process, in the cache memory, multiple image pixels through at least the first layer in parallel using multiple execution threads.

### Claim 18 (depends on 17)

The device of claim 17, wherein multiple consecutive layers of the convolutional neural network are cached and processed simultaneously.

### Claim 19 (depends on 18)

The device of claim 18, wherein the multiple consecutive layers are 3->32 and 32->64.

### Claim 20 (depends on 16)

The device of claim 16, wherein the device is a color printer.